neighborhood disorder, crime and the broken …essay.utwente.nl/66462/1/meinen_ma_mb.pdf1 | p a g e...
TRANSCRIPT
1-12-2014
Neighbourhood disorder, crime and the
Broken Windows Theory An examination into the relationship between neighbourhood disorder and crime in the city districts of Rotterdam
Thijs Meinen UNIVERSITEIT TWENTE
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Neighbourhood disorder, crime and the Broken Windows Theory
An examination into the relationship between neighbourhood disorder and crime in
the city districts of Rotterdam
Author
Name: T.Meinen
Student number: 1388894
Location: University of Twente, Enschede
Faculty: Faculty of Management and Governance
Master: Public Administration
Specialization: Public Safety
Study year: 2014-2015
Supervisors
First supervisor: Dr. Guus Meershoek
Second supervisor: Dr. Jörgen Svensson
Version information
Version: First draft
Date: December 1st, 2014
Description: Master Thesis
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Preface The report that lies in front of you is my master thesis. This research is the final product of the master
Public Administration. When I earned my bachelor degree in Applied Safety and Security Studies at the
Saxion University in Enschede in 2012, I decided to continue studying. The choice fell upon the study
Public Administration at the University of Twente. Roughly two and a quarter of a year later, is this the
final result. The specialization of my master program is Public Safety and this research is therefore
related to this topic. This final research concludes a period as a student for me and starts a new period
as a master of science in the Public Administration.
This report is about the relationship between neighbourhood disorder and crime in the city districts of
Rotterdam during the period 2006-2011. Main inspiration for this research is the Broken Windows
Theory of Wilson and Kelling (1982) about how minor forms of public disorder (e.g. broken windows)
could lead to severe crime and a downward spiral of urban decay. The focus of this research is on the
mutual relationships of the variables of the Broken Windows Theory, especially on the relationship
between neighbourhood disorder and crime, in the city districts of Rotterdam during this period.
This research has been performed internally at the University of Twente. A special thanks goes to Guus
Meershoek and Jörgen Svensson for supervising this project and for the useful feedback and tips during
this research. I also want to thank Arianne Need for the help she provided in setting up this research.
I would like to thank Suzanne van den Berge for helping me collect the data that was required for
carrying out this research. Last but not least, I want to thank my family for supporting me during this
period. A special thanks goes out to Bart Meinen, Jurgen Meinen, Jan Hendriks, Daan Jan Hendriks and
Marion Hendriks for providing feedback and checking my work in the completion of this research.
Enschede, december 1st, 2014
Thijs Meinen
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Summary
During the years 2002 - 2005 many changes were made regarding safety in the city of Rotterdam. The
most important events during these years comprised the municipal elections of Rotterdam in 2002.
Leefbaar Rotterdam won the majority of seats in these elections, the party formed a coalition with the
Christian Democrats (CDA) and the Liberal Party (VVD), together creating ‘The new Elan of Rotterdam’.
Safety was given the highest priority in the coalition agreement; specific targets were formulated and
special funds were made available in order to guarantee this priority.
The new approach to safety seemed to work, because the ratings of the Safety Index grew from 5.6 in
2002 to 6.9 in 2005, reflecting the current state of affairs of safety in Rotterdam.
The changes that were made and the attention that was given to safety improved the safety in the city,
but room for improvement remains. Rotterdam citizens gave ratings to livability and feelings of safety
in the neighbourhood that were below the average in the Netherlands during recent years (Centraal
Bureau voor de Statistiek, 2013b, 2014). Major problems according to the citizens of Rotterdam still
remain crime, social unsafety, drug nuisance, pollution and management of the public space (COS,
2013). Deterioration is also seen as a problem among the citizens of Rotterdam. Key aspects of the
safety policy since 2002 aimed to make Rotterdam cleaner and keep it more undamaged. More
undamaged means for instance, less broken or damaged bus stops or broken street furniture.
To examine the effect of this kind of policy on crime, this research examines the physical and social
decay of a neighbourhood and the impact of neighbourhood disorder on crime in different districts of
Rotterdam. The following research questions are examined with this research:
- To what extent did neighbourhood disorder change in the districts of Rotterdam in the period
of 2006-2011 and
- Does neighbourhood disorder explain crime changes?
The starting point for examining above relationship is the Broken Windows Theory (Wilson & Kelling,
1982). This Theory consists of four important variables: disorder, fear of crime, social control and
crime. Hypotheses are formulated to test the relationships between these four variables, suggesting a
positive relationship between the variables, except for social control, as the relationship between
social control and the other variables is expected to be negative.
The basic structure of this research is a cross-sectional design. Univariate analyses are used to illustrate
the development of neighbourhood disorder and crime. Bivariate analyses are used to test the
correlation between the variables of the Broken Windows Theory, which includes the relationship
between neighbourhood disorder and crime. The dependent variable crime is divided into four crime
types:
- Theft and burglary crimes;
- Destruction or material damage against the public law and order;
- Violence and sex crimes;
- Drugs and alcohol offenses.
The results of the univariate analyses of crime and disorder are as follows: the overall crime rates
declined with 10,1 percent in Rotterdam in the period of 2006-2011, there were some fluctuations
however. Theft and burglary were the most reported types of crime, whereas violence and sex crimes
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were the most declining types of crime. The second most declining types of crime were destruction
and damage against public law and order followed by theft and burglary crimes. Drugs and alcohol
offenses were more reported in this period.
The perceived disorder fluctuated in the districts of Rotterdam in the period 2006-2011. However,
disorder was more perceived in 2011 than in 2006, meaning disorder has increased in this period. The
highest point of perceived disorder was observed in 2008. The four districts with the most perceived
disorder were Charlois, Delfshaven, Feijenoord and Stadscentrum. Disorder is more perceived to be a
neighbourhood problem in almost all the districts. The district with the biggest increase of disorder
was Hoek van Holland. The average increase of disorder in the period 2006-2011 was 17,1%.
The bivariate analyses demonstrated that the variables of the Broken Windows Theory are mutually
correlated, confirming almost all hypotheses. Social control was not correlated with theft and burglary
crimes, nor with destruction or material damage against the public law and order crimes. The fact that
the variables of the Broken Windows Theory are correlated indicates that neighbourhood disorder and
crime are also correlated. Drugs and alcohol offenses showed to have a strong correlation with
disorder. Violence and sex crimes as well as theft and burglary crimes showed to have a moderate
correlation with disorder. Destruction or damage against the public law and order had a weak
correlation with disorder, whereas disorder and fear of crime showed to be the most correlated with
crime. Disorder was even correlated with crime after correcting for fear of crime for? Violence and sex
crimes, and drugs and alcohol offenses. To what extent neighbourhood disorder can explain crime
cannot be discussed in terms of causation due to the cross-sectional design, but out of the variables of
the Broken Windows Theory disorder was the best predictor for crime. The correlations between the
variables of the Broken Windows Theory imply that the theory can explain the relationship between
neighbourhood disorder and crime. The variables of the Broken Windows Theory are closely connected
to each other, the analyses also demonstrated that disorder is moderately correlated with social
control and strongly correlated with fear of crime.
The contribution of this research to the field of neighbourhood safety is confirmation of the mutual
relationships between the variables associated with the Broken Windows Theory. The results found in
this study are consistent with the majority of earlier conducted studies regarding this topic. The
confirmation of the mutual relationships of the Broken Windows Theory and the significant positive
relationship of neighbourhood disorder with crime after correction for fear of crime makes it plausible,
also in Rotterdam, that neighbourhood disorder leads to crime and a downward spiral of urban decay
(Wilson & Kelling, 1982).
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Samenvatting
In de periode van 2002 tot en met 2005 is er veel veranderd betreffende veiligheid in Rotterdam. Een
belangrijke gebeurtenis was de gemeenteraadsverkiezing van 2002 in Rotterdam. Bij deze verkiezingen
won Leefbaar Rotterdam verrassend de meeste zetels en zij vormden een coalitie met het CDA en de
VVD. Dit rolde uit in het coalitieakkoord ‘een nieuw eland voor Rotterdam’. In dit coalitieakkoord werd
aan veiligheid de hoogste prioriteit gesteld in het beleid van de gemeente. Daarnaast werden er veel
doelen geformuleerd en werd er geld vrijgemaakt om een veiligere stad te maken van Rotterdam.
De nieuwe veiligheidsaanpak sloeg aan en dit was terug te zien aan de cijfers van de veiligheidsindex.
Het cijfer van de veiligheidsindex steeg van een 5.6 in 2002 naar een 6.9 in 2005. Deze index beschrijft
de huidige veiligheidssituatie in Rotterdam en geeft daaraan een cijfer. Alhoewel de veranderingen en
de toegenomen aandacht voor veiligheid, de veiligheid in de stad verbeterde, is er nog steeds ruimte
voor verbetering. De laatste jaren beoordelen de burgers van Rotterdam de leefbaarheid en de
veiligheidsbeleving lager dan het gemiddelde in Nederland (Centraal Bureau voor de Statistiek, 2013b,
2014). De grootste problemen in de stad volgens de burgers is nog steeds criminaliteit, sociale
onveiligheid en drugsoverlast, en vervuiling van de openbare ruimte en het beheer van de openbare
ruimte (COS, 2013). Ook verloedering en verpaupering van wijken wordt nog steeds als een probleem
gezien onder de burgers van Rotterdam. Terwijl een van de sleutel aspecten van het veiligheidsbeleid
sinds 2002 was dat Rotterdam schoner en heler moet zijn.
Om te onderzoeken of dit soort beleid effect heeft op criminaliteit, wordt er in dit onderzoek
onderzocht of de sociale en fysieke verval van een wijk invloed heeft op criminaliteit. Dit betekent dat
de impact van wanorde op criminaliteit wordt onderzocht in de deelgemeenten van Rotterdam. De
volgende vraag stond centraal in dit onderzoek:
In welke mate veranderde de wanorde in de deelgemeenten van Rotterdam in de periode van 2006-
2011 en verklaart wanorde veranderingen in criminaliteit?
Het startpunt van het onderzoeken van de relatie tussen wanorde en criminaliteit is de Broken
Windows Theory. Deze theorie gaat over hoe kleine vormen van publieke wanorde (bijv. kapotte
ramen) kunnen leiden tot ernstige criminaliteit en een neerwaartse spiraal van stedelijk verval. De
theorie bestaat uit vier variabelen: wanorde, onveiligheidsgevoel, sociale controle en criminaliteit. Om
de relaties tussen deze variabelen te meten zijn er hypotheses opgesteld. Deze hypotheses verwachten
een positieve relatie tussen de variabelen, behalve tussen sociale controle. Tussen sociale controle en
de andere variabelen wordt een negatieve relatie verwacht. De verwachting is dat de variabelen van
de Broken Windows Theory onderling correleren.
De onderzoeksopzet is een cross sectioneel ontwerp. Univariate analyses zijn gebruikt om de
ontwikkeling van wanorde en criminaliteit in de deelgemeenten van Rotterdam tussen 2006 en 2011
weer te geven. Bivariate analyses zijn gebruikt om de correlatie testen tussen de variabelen van de
Broken Windows Theory. Door deze test wordt ook de relatie tussen wanorde en criminaliteit getest.
De afhankelijke variabele criminaliteit is verdeeld in vier criminaliteitssoorten: vermogensdelicten,
vernielingen en misdrijven tegen openbare orde en gezag, gewelds- en seksuele misdrijven, en drugs
en alcoholovertredingen.
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De volgende resultaten zijn gevonden met de univariate analyses. De criminaliteitscijfers zijn gedaald
met 10,1 procent in Rotterdam in de periode 2006-2011. Echter schommelde de ontwikkeling van
criminaliteit in deze periode. Van de soorten criminaliteit zijn vermogensdelicten het meeste gemeld
en had gewelds- en seksuele misdrijven de sterkste daling. De soort criminaliteit dat had de een na
sterkste daling is vernielingen en misdrijven tegen openbare orde en gezag gevolgd door
vermogensdelicten en drugs- en alcoholovertredingen. De criminaliteitssoort met de meest
consistente daling is vernielingen en misdrijven tegen openbare orde en gezag gevolgd door
vermogensdelicten, gewelds- en seksuele misdrijven, en drugs- en alcohol overtredingen zijn zelfs
toegenomen.
De waargenomen wanorde in de deelgemeenten van Rotterdam schommelde in de periode van 2006
tot en met 2011. Echter is wanorde meer waargenomen in 2011 dan in 2006. Dit betekent dat de
disorder is gestegen in deze periode. In 2008 is wanorde het meeste waargenomen. De vier
deelgemeenten met de meeste waargenomen wanorde zijn Charlois, Delfshaven, Feijenoord en
Stadscentrum. Wanorde is elke deelgemeente meer waargenomen als een buurtprobleem. De
deelgemeente waarin wanorde het sterkst is gestegen is Hoek van Holland. De gemiddelde stijging van
wanorde bedraagt 17,1 procent in de deelgemeenten van Rotterdam in deze periode.
De bivariate analyses demonstreerden dat de variabelen van de Broken Windows Theory onderling
zijn gecorreleerd. Daardoor zijn bijna alle hypotheses volledig bevestigt. Sociale controle correleerde
niet met vermogensdelicten en vernielingen en misdrijven tegen openbare orde en gezag. Dat de
variabelen van de Broken Windows Theory onderling correleren, betekent dat wanorde en criminaliteit
ook correleren. Drugs- en alcoholovertredingen toonde aan een sterke correlatie te hebben met
wanorde. Vermogensdelicten en gewelds- en seksuele misdrijven toonden aan een matige correlatie
te hebben met wanorde. Vernielingen en misdrijven tegen openbare orde en gezag had een zwakke
correlatie met wanorde. Wanorde en onveiligheidsgevoel correleerden het sterkst met criminaliteit.
Wanorde correleerde zelfs met drugs- en alcoholovertredingen en gewelds- en seksuele misdrijven na
het controleren op onveiligheidsgevoel. De mate waarin wanorde verklaart criminaliteit kan niet
worden besproken in termen van causaliteit door het cross sectionele ontwerp van dit onderzoek.
Desalniettemin bleek wanorde van de variabelen van de Broken Windows Theory de beste voorspeller
van criminaliteit. De gevonden correlaties tussen de variabelen van de Broken Windows Theory
impliceren dat de Broken Windows Theory de relatie tussen criminaliteit en wanorde zou kunnen
verklaren. Deze variabelen zijn nauw verbonden. De analyses demonstreerden ook dat wanorde een
matige correlatie heeft met social control en een sterke correlatie met onveiligheidsgevoel.
De bijdrage van dit onderzoek aan het veld van wijkveiligheid is de bevestiging van de onderlinge
relaties tussen de variabelen die worden geassocieerd met de Broken Windows Theory. De gevonden
resultaten zijn consistent met de meerderheid van de eerder uitgevoerde onderzoeken betreffende
dit onderwerp. De bevestiging van de onderlinge relaties van de variabelen van de Broken Windows
Theory en de significante positieve relatie tussen wanorde en criminaliteit na het controleren op
onveiligheidsgevoel, maken het aanneembaar dat, ook in Rotterdam, wanorde leidt tot criminaliteit
en een neerwaartse spiraal van stedelijk verval (Wilson & Kelling, 1982).
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Table of contents Preface ................................................................................................................................................. 2
Summary ............................................................................................................................................. 3
Samenvatting ....................................................................................................................................... 5
1. Introduction ..................................................................................................................................... 9
1.1 Background .............................................................................................................................. 9
1.2 Research aim ......................................................................................................................... 11
1.3 Research questions ............................................................................................................... 12
1.3.1 Central research question ............................................................................................. 12
1.3.2 Sub-questions ................................................................................................................ 12
1.4 Outline of this report ............................................................................................................. 12
2. Theoretical Framework ................................................................................................................. 13
2.1 Concept of disorder ............................................................................................................... 13
2.2 The Broken Windows Theory ................................................................................................ 14
2.2.1 Fear of crime.................................................................................................................. 15
2.2.2 Social control ................................................................................................................. 16
2.3 Discussion in the literature .................................................................................................... 17
2.4 Hypotheses ............................................................................................................................ 19
2.5 Conclusion ............................................................................................................................. 20
3. Data and operationalization .......................................................................................................... 22
3.1 Units of analysis ..................................................................................................................... 22
3.2 Operationalization ................................................................................................................. 23
3.2.1 Crime ............................................................................................................................. 23
3.2.2 Neighbourhood disorder ............................................................................................... 23
3.2.3 Fear of crime.................................................................................................................. 24
3.2.4 Social control ................................................................................................................. 24
3.3 Research design ..................................................................................................................... 25
3.3.1 Data collection ............................................................................................................... 25
3.3.2 Statistical analysis .......................................................................................................... 26
4. Variation and development of crime in Rotterdam between 2006 and 2011 .............................. 28
4.1 Development of crime in Rotterdam .................................................................................... 28
4.2 Magnitude of the crime decline ............................................................................................ 29
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4.2 Fluctuations in the crime decline .......................................................................................... 30
4.3 Extensiveness of the crime decline ....................................................................................... 31
4.4 Conclusion ............................................................................................................................. 34
5. Variation and development of neighbourhood disorder in Rotterdam between 2006 and 2011 35
5.1 Development of neighbourhood disorder in Rotterdam ...................................................... 35
5.2 Development of social disorder in Rotterdam ...................................................................... 36
5.3 Development of physical disorder in Rotterdam .................................................................. 37
5.4 Magnitude of the change of disorder ................................................................................... 39
5.5 Conclusion ............................................................................................................................. 40
6. Results ........................................................................................................................................... 41
6.1 Neighbourhood disorder and crime ...................................................................................... 41
6.2 Conclusion ............................................................................................................................. 46
7. Conclusion, discussion, contribution, and limitations of this research ......................................... 47
7.1 Conclusion ............................................................................................................................. 47
7.2 Contributions to the literature .............................................................................................. 47
7.3 Recommendations ................................................................................................................ 49
7.4 Limitations ............................................................................................................................. 51
References ............................................................................................................................................. 53
Appendix A Description of the districts of Rotterdam ...................................................................... 56
Appendix B Overview of the variables .............................................................................................. 60
Appendix C Table belonging to figures in chapter 4 ......................................................................... 62
Appendix D Tables belonging to chapter five ................................................................................... 66
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1. Introduction This chapter describes the design of the research leading to this master thesis. First, the background of this research will be explained in paragraph 1.1. Subsequently, the aim of this research will be discussed in paragraph 1.2. Based on the research aim a central research question with several sub-questions has been formulated as described in paragraph 1.3. Finally, the outline of this report will be discussed in paragraph 1.4.
1.1 Background Since the municipal elections of 2002, a lot has changed in the Dutch city of Rotterdam regarding safety. The previous Board of Mayor and Alderman gave safety the status of a program. This meant that six themes regarding safety were elaborated in a project book, which led to approximately thirty projects. The control of these projects was in the hands of the Steering Committee Safety, which was chaired by the Mayor. The main target was to improve the grade of safety from a six to a seven on a ten-point scale. In 2002 the safety index was introduced in Rotterdam. This index describes the current state of affairs of safety in Rotterdam and gives a score to the safety in Rotterdam. According to Mayor Opstelten, this target was barely operationalized (Tops, 2007). The used approach was not successful and resulted only in a displacement of the problems. Another problem of the safety policy was the excessive number of projects. In many cases there was uncertainty if there were financial means available to execute a project. In other cases it was uncertain if a project was executed at all (Tops, 2007). To make the first steps towards a safer Rotterdam, a conference was held in Kaatsheuvel in 2001, where a five-year plan was put together to improve the safety in the city. The actual change began after the municipal elections in 2002. The political party ‘Leefbaar Rotterdam’, led by Pim Fortuyn, won the majority of seats in the city council, which was a big surprise. In that period, Rotterdam was the city with the highest crime rates and most unsolved murders in the Netherlands. Fortuyn thankfully used this fact and made safety the primary priority of the campaign of ‘Leefbaar Rotterdam’ in the election campaign. After winning the elections ‘Leefbaar Rotterdam’ formed a coalition with the political parties the Christen Democrats Appel (CDA) and the Liberal Party (VVD) after the elections, which resulted in the coalition agreement ‘The new Elan of Rotterdam’. It was based on the five-year plan that was made in Kaatsheuvel in 2001. The coalition agreement had five key elements: Safety, Housing, Education and Youth, Economic development, and Integration. Safety was the element with the highest priority. The elaboration of the coalition agreement resulted in nineteen targets. The Police and Justice departments were also involved in determining these targets. This was to ensure that the targets were decided not only by politicians, but also by the organizations, which had to execute and achieve them. Some examples are:
Rotterdam has to be measurable safer in 2005: The safety index of all the districts and neighbourhoods should be equal or higher than in June 2002;
City marines will be used in the most unsafe districts. City marines are civil servants who can speed up processes with an amount of money to get results. Their job is to secure that the district which they are assigned to is safe;
The city has to be cleaner and more undamaged;
100 million euro’s was made available for tackling the safety issues. The autumn of 2003 and the spring of 2004 were important periods in the safety approach of Rotterdam. There were some important events which reduced pressure of the safety approach. Some structural changes were also made. First Alderman de Faria was removed. Stakeholders saw Alderman de Faria as a disturbing factor and collaborating with her was perceived as impossible in this highly-strung period. There were also good results of the safety index. Improvement before the latest safety index measurement was minimal and it was about time that substantial results were achieved. At the
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17th of Mai 2003 the new safety index was published and there was a significant difference. It improved from a 5.6 in 2002 to a 6.2 in 2003. This showed that the safety approach was effective, although there was still a lot of work to be done. A structural change was the particularizing and focusing of the neighbourhood safety action programs (WVAP’s). The WVAP’s were more specific and tailored to the needs of the different districts. Another structural change was the normalization of the relationship between the council of Mayor and Alderman and the civil service. Additionally, the city mariners were gradually more accepted and it showed that the city mariners were not the tough civil servants who put things in order as feared. Besides, the city mariners were able to build workable and valued relationships with the civil services and districts. After the events in the autumn of 2003 and the spring of 2004, the safety approach stood on firm ground and could function to full extend (Tops, 2007). The safety index of 2004 showed another improvement to a score of 6.6 and the safety index of 2005 again increased to a 6.9. Mayor Opstelten was regarded as a key figure and the driving force of the safety approach of Rotterdam. He had most of the roles in the safety approach. He was the administrative body who was charged with maintaining of the public order in Rotterdam. He was also the chairman of the City Council, the Board of Mayor and Alderman, the Safety Steering Committee, the tripartite consultation with the chief commissioner of the Police and the Chief Prosecutor, Mayor consultation with the chairmen of the districts and he had often consultations with stakeholders from the city of Rotterdam. Mayor Opstelten was praised for his sharpness, managerial insight, he never slacked and he gave incisively comments (Tops, 2007). Just before the elections of 2005, a second five-year plan was made and this plan was in line with the previous five-year plan. The Labor Party won the elections and formed a coalition with the Christen Democratic Appel, Groen Links and the Liberal party. The college program did not change much with regard to the previous college program. Ambitious targets were also made this time. The most important target was that there should be no more unsafe neighbourhoods in Rotterdam according to the scores of the safety index of 2010. Eventually was this target achieved. The changes in and attention for safety in Rotterdam improved the safety in the city (Tops, 2007). Rotterdam improved a lot concerning safety and well-being of the people since 2002 (Centrum voor Criminaliteitspreventie en Veiligheid, 2013). The amount of robberies and burglaries decreased in the first three months of 2013 with regard to the same period in 2012. The fact that Rotterdam improved a lot, does not mean there is no more work to be done. The well-being increased the last years, but the residents of the police-region Rotterdam-Rijnmond are on average less positive about their neighbourhood than the residents of other police-regions in the Netherlands (Roodenburg, 2012). The thing which was most seen in the city of Rotterdam was deprivation. Clutter on the streets and graffiti were seen as the most annoying factors for the residents. This was mostly the case in neighbourhoods which were considered to be the most deprived. The livability of neighbourhoods in Rotterdam scores a 7.1 in general on a ten-point scale (Centraal Bureau voor de Statistiek, 2014). At first sight this seems to be a good score, but in comparison with all the other municipalities and regions of the Netherlands it is the lowest. Rotterdam has also a significant higher amount of physical decay and social nuisance in neighbourhoods than the average in the Netherlands. 27,9% of the residents of Rotterdam experiences physical decay in their neighbourhood and 12,5% experiences social nuisance. The average in the Netherlands for physical decay is 24.4% and 9,8% for social nuisance People in Rotterdam perceive a higher amount of feelings of unsafety compared to the average in the
Netherlands. The average percentage of the citizens in the Netherlands who feel sometimes or often
unsafe in their neighbourhood is 18,8%, in Rotterdam this is 21.8%. On all the indicators which the
Safety Monitor 2013 used to measure the feelings of unsafety, Rotterdam scores significant higher
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than the average of the Netherlands. These indicators are places where people are feeling themselves
unsafe, for instance places where groups of youth are holding up or in the public transport. The
perception of crime among the residents of Rotterdam is also less positive than the average in the
Netherlands: 14.3% of the residents of Rotterdam thinks that a lot of crime occurs in their
neighbourhood where as the average of the Netherlands is 9.8%. Noteworthy is the fact that 16.8%
thinks that the amount of crime has increased where the objective crime rates suggests that this is
decreasing (Centraal Bureau voor de Statistiek, 2013a).
The crime rates are decreasing in Rotterdam and the livability has increased, but the biggest problems in the city according to the citizens of Rotterdam are still crime, social unsafety and drugs related nuisance, pollution and the management of the public space (COS, 2013). Deterioration is also seen as a problem among the citizens of Rotterdam. One of the key aspects of the safety policy since 2002 was that Rotterdam has to be cleaner and more undamaged. To examine if this kind of policy has an effect on crime, this research will examine the impact of neighbourhood disorder on crime in the districts of Rotterdam.
1.2 Research aim In the background it is addressed that Rotterdam scores for certain safety related topics such as safety feelings, perception of crime and livability lower than the average in the Netherlands. In addition, the citizens of Rotterdam experience more social nuisance and physical decay than the average in the Netherlands. Due to the fact that a cleaner and more undamaged city is a key aspect in the safety policy of Rotterdam since 2002, this research examines the relationship between neighbourhood disorder and crime. This relationship is interesting, because crime is steadily decreasing the last years in the Netherlands, but the amount of citizens that feel themselves unsafe does not decrease (Bierling, Lucas, & Tops, 2013). This is among other things due to the presence and visibility of disorder in neighbourhoods. The research aim is twofold. First, the changes in neighbourhood disorder and crime in the city of Rotterdam between 2006 and 2011 will be examined. Second, the impact of neighbourhood disorder on crime will be analyzed. To achieve this, the city of Rotterdam is, like already mentioned earlier, chosen to be examined. The reasons for this are practical. Rotterdam is a city with a diverse variety of ethnicities, problems and crime. The availability of data about the citizens of Rotterdam played also a part in the choice of this city. Neighbourhood disorder can roughly be seen as the sum of physical decay and social nuisance in a neighbourhood. This concept will be elaborated in chapter two. The changes in neighbourhood disorder and crime will be examined between 2006 and 2011, because this gives information about trends and developments of crime and neighbourhood disorder. In addition, the districts of Rotterdam differ in composition, density and surface. Therefore gives the examination of these changes information about the differences between the districts regarding the number of crime and the degree in which disorder is perceived. Another reason why the changes in neighbourhood disorder are being examined, is to see if neighbourhoods with more perceived disorder also have a higher crime rate. When this is the case, there could be an association between crime and neighbourhood disorder. This is the second goal. If an association does exist, this could give implications for policies regarding neighbourhoods, safety and crime. The research objectives can be summarized as follows: examine to what extent neighbourhoods with a higher level of disorder also have a high level of crime in the districts of Rotterdam between 2006 and 2011. Based on the findings, recommendations are made to improve the policies about neighbourhoods with regard to safety and crime.
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1.3 Research questions On the basis of the research aim the next questions can be drafted. These are divided in a central- and sub-questions.
1.3.1 Central research question To achieve the afore mentioned goals the following central research question is stated: To what extent does neighbourhood disorder change in the districts of Rotterdam in the period of 2006-
2011 and does neighbourhood disorder explain crime changes?
1.3.2 Sub-questions In order to answer the central research question the following sub-questions are formulated:
1. What is neighbourhood disorder and what is theoretically and empirically known about the impact of neighbourhood disorder on crime?
2. What is the variation in crime rates and how have the crime rates developed in the districts of Rotterdam between 2006 and 2011?
3. What is the variation of neighbourhood disorder and how has neighbourhood disorder developed in the districts of Rotterdam between 2006 and 2011?
4. What is the relationship between neighbourhood disorder and crime in the districts of Rotterdam between 2006 and 2011?
1.4 Outline of this report This report consists of seven chapters. This chapter sketched the situation regarding safety and crime
in Rotterdam before the examined period of this research. Further introduced this chapter the concept
of neighbourhood disorder, the main question and the research aim. This research is focused on the
relationship between neighbourhood disorder and crime. Therefore needs the concept of
neighbourhood disorder be further explained. In addition, the relation between neighbourhood
disorder and crime will be discussed on the basis of the available literature regarding this topic and
hypotheses will be stated to examine the relationship between neighbourhood disorder and crime.
This will happen in chapter two. The way in how this research is conducted will be discussed in chapter
three. This chapter focuses on the units of analysis, the operationalization of the variables and the
research methodology. Chapter four describes the variation and development of crime in Rotterdam.
This chapter focuses on the magnitude of the crime decline in Rotterdam, the fluctuations in this crime
decline and the extensiveness of the crime decline in the districts of Rotterdam. The fifth chapter
describes the variation of neighbourhood disorder and how this develops in Rotterdam. This chapter
will take a closer look at the development of social and physical disorder in the districts of Rotterdam.
After the describing chapters an explanatory chapter follows in which the hypotheses are tested or in
other words where the impact of neighbourhood disorder on crime will be tested. This will happen in
chapter six. The last chapter gives the conclusion of this research and will discuss the results, the
limitations and the contribution of this research to this field of research.
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2. Theoretical Framework
This chapter describes the theoretical framework on which this research is based. At first the concept
of disorder will be elaborated in paragraph 2.1. Subsequently in paragraph 2.2 the Broken Windows
Theory will be explained. In paragraph 2.3 the disagreement in the literature about the Broken
Windows Theory will be discussed. Thereafter are hypotheses stated based upon this theoretical
framework in paragraph 2.4. Finally in paragraph 2.5 the following research question will be answered:
What is neighbourhood disorder and what is theoretically and empirically known about the impact of
neighbourhood disorder on crime?
2.1 Concept of disorder The streets, sidewalks and parks do not belong to anyone and therefore to everyone. The visual
salience and symbolism with regard to such places is what makes disorder of theoretical interest
(Sampson & Raudenbush, 1999). Even if we do not want it to be so, disorder triggers attributions,
predictions and prejudices in the minds of insiders as well as outsiders. Disorder changes the calculus
of investors, real estate agents, future home buyers and insurance agents and it shapes perceptions of
residents who consider moving out of the neighbourhood. Signs of disorder are also a cue of the
effectiveness of residents who are seeking neighbourhood improvement, and that record may
discourage or encourage future activism among residents. This means that disorder is fundamental to
the general understanding of urban neighbourhoods.
Since the early 2000s, there has been an increasing discussion about the definition of neighbourhood
disorder. Some studies split neighbourhood disorder in two components: physical and social (Chappell,
Monk‐Turner, & Payne, 2011; Skogan, 1990). The definition of Skogan (1990) refers to visible
indications of a lack of order and social control in the community. In this case order is a situation of
safety, tranquility, and obedience of the law. Social control is about maintaining this order. Skogan
states that disorder has a physical and a social dimension and that both dimensions signal a breakdown
of the social order in a neighbourhood. Social disorder is a matter of behavior and physical disorder
involves physical signs of unchecked decay (Skogan, 1990).
Ross and Mirowsky (1999) defined social disorder as signs that indicate a lack of social control which
involves people. Examples are fights, disturbances among neighbors and people who are hanging out
on the streets, drinking, consuming drugs and people who produce a sense of danger (Ross &
Mirowsky, 1999). Ross and Mirowsky (1999) refer to physical disorder as the entire physical
appearance and decay of a neighbourhood. Neighbourhoods with a high level of physical disorder are
considered dirty and noisy. Vandalism and graffiti are common and in these neighbourhoods often
broken street furniture, bus stops and abandoned buildings can be found. These are also indicators
that social control has broken down (Ross & Mirowsky, 1999).
In literature on disorder and crime there is also attention for structural constraints and in particular on
structural dimensions that have an economic nature. Residents have little control over these elements
(Sampson & Raudenbush, 1999). A structure which often is mentioned is concentrated poverty.
Economic deprivation is relevant in relation to disorder, because cleaning up residential and
commercial areas and repairing buildings requires money. In addition, for in areas of concentrated
disadvantage it is often difficult to support viable commercial enterprises, and many apartments and
stores are vacant which gives little incentives to investors to repair their properties. However,
structural constraints are not only about economic features. A feature that is considered important for
14 | P a g e
urban social organization is residential stability (Wilcox, Quisenberry, Cabrera, & Jones, 2004). Low
transience and high levels of home ownership work together to inbreathe a stake of conformity among
residents, in this case to neighbourhood well-being. Local ties and attachment are also linked to
residential stability (Taylor, 1997). A number of other structural constraints affect the ability of a
neighbourhood to counteract public incivilities including nonresidential land use, population that
makes use of local services, the sheer density of a population and the immigrant’s concentration. The
differential land use of cities is important to comprehend neighbourhood crime and disorder patterns,
because illegal activities feed the temporal and spatial structure of routine legal activities (Sampson &
Raudenbush, 1999). This means that the effects of concentrated residential instability and resource
disadvantage should be considered in relation with structural characteristics such as density.
Despite structural constraints, one might see human agency as the central explanation of
neighbourhood disorder. In this perspective, residents do not only face ecological structures or
material circumstances, but also face the challenge to organize themselves in order to achieve shared
public ends. In this research the formulation of van Stokkom and Toenders (2010) will be adopted and
refer to social control as the capacity of a social unit to regulate itself, monitoring and addressing
people on unacceptable activities. Social control is also about the realization of collective goals. The
desire of residents of a neighbourhood to live in safe environments free of crime and disorder is one
of the most central of common goals (Sampson & Raudenbush, 1999). The shared willingness of local
residents to take action for the common good depends on mutual trust and the social cohesion among
neighbors (Sampson & Raudenbush, 1999). It is unlikely that someone takes action in a neighbourhood
context where people mistrust each other and the rules are unclear.
Process-oriented mechanisms such as social control and structural constraints are not mutually
exclusive. A more plausible way of thinking is that human agency and structural constraints are
interrelated. It is likely that contributions of social control and structural characteristics could explain
disorder and crime. Sampson, Raudenbush, and Earls (1997) suggest that residential instability and
concentrated resource disadvantage are major structural conditions that undermine social control, in
turn promote crime and disorder.
In literature it is unclear if disorder and crime are distinct concepts or that they are basically at opposite
ends of the same continuum. Major crimes could be seen as the extreme end of the disorder
continuum, but they are rare and mostly unseen (Gau & Pratt, 2008). Sampson and Raudenbush (1999)
state that it is a reasonable hypothesis that public disorder and predatory crimes are phenomena of
the same explanatory process and therefore at different ends of the same continuum. In addition it
seems Sampson and Raudenbush unpersuasive that graffiti causes robbery, it is more likely to assume
that a lack of social control caused graffiti and robbery. Gau and Pratt (2008) also observed that citizens
do not see the difference between disorder and crime, in the view of the citizens those two
phenomena blend together. In this case disorder and crime are only labels used by criminologists and
police agencies in their practices.
2.2 The Broken Windows Theory Keizer, Lindenberg and Steg (2008) performed a study in which was showed that disorder changes
people’s behavior. Six experiments were performed in this study and the conclusion based on these
experiments was that ‘signs of inappropriate behavior like graffiti or broken windows lead to other
inappropriate behavior (e.g., litter or stealing), which in turn results in the inhibition of other norms
(i.e., a general weakening of the goal to act appropriately)’ (Keizer, Lindenberg, & Steg, 2008). The
15 | P a g e
question in this research is: does disorder also lead to more crime in neighbourhoods? A theory which
addresses this question is the Broken Windows Theory (Wilson & Kelling, 1982).
The Broken Windows Theory suggests that minor forms of public disorder (e.g. broken windows) could
lead to severe crime and a downward spiral of urban decay. Wilson and Kelling (1982) suggest ‘that
untended behavior leads to the breakdown of community controls’. For instance, when trash is not
picked up and loiterers are not asked to move on, this invites more thrash being thrown on the ground
and more loiterers to gather. As this disorder increases it sends a message to the inhabitants that the
situation is escalating and that the social control in their neighbourhood is failing. A key part of this
theory is the perception of untended disorder (Weisburd, Hinkle, Famega, & Ready, 2010).
Perceived disorder will lead inhabitants to think that crime is on the rise and therefore they will modify
their behavior. These inhabitants will use the streets less often, become less likely to intervene against
disorderly people and in some cases this could lead to that ‘good’ inhabitants move away. This results
in a lowering of informal social control, which could lead to more disorder and crime, because people
see that in these areas they can get away unpunished with such behavior. Eventually criminals could
see such a neighbourhood as a good place to work with impunity.
Another key part of the theory is fear of crime. Through the perceived disorder people retreat to their
homes, because they think the streets are not safe. Doran and Lees (2005) investigated if spatial-
temporal links exist between disorder, crime and the fear of crime. It turned out that places that were
avoided by people in the evening, were places with higher levels of disorder than places that were not
avoided. This study showed that places that are avoided by people have the potential for the crime or
disorder concentrations to increase over time. This is due to the lack of natural surveillance in these
areas and this makes it easier for disorderly acts to take place or criminals to operate.
Places that are fearful and therefore avoided are places were disorder and crime could break down
the mechanisms of social control. As citizens withdraw or avoid places, they also withdraw from their
duty of mutual support with fellow citizens on the streets and as a result they are relinquishing the
social controls of the neighbourhood which they formerly helped to maintain. What follows is that
crime and fear of crime isolate residents and this leads to the undermining of mutual trust and
solidarity among neighbors (Xu, Fiedler, & Flaming, 2005). The social cohesion is out of the question
when people stop to interact with one another and this also counts for the shared willingness to
engage in social control of public areas. The Broken Windows Theory could be summarized by figure
1.
Figure 1: The Broken Window Theory (Weisburd et al., 2010)
2.2.1 Fear of crime The Broken Windows Theory claims that perceived disorder makes residents fearful and withdraw
themselves into their homes. Therefore it could be stated that fear of crime is a key part of the Broken
Windows Theory. Fear of crime could be defined as ‘an emotional reaction characterized by a sense of
Disorder goes untreated and is
noticed by residents
Citizens become fearful and
withdraw from the community
Informal social control decreases
and/or is perceived to be low by criminals
Disorder and crime increase as criminals Increase
their activity in the area
16 | P a g e
danger’ (Garofalo, 1981). There is evidence that disorder is linked to fear of crime (Perkins, Meeks, &
Taylor, 1992; Skogan, 1990; Wyant, 2008). Neighbourhood disorder is likely to affect an individual’s
perception of their community due to it is commonplace and visible and crime is a relatively rare and
mostly invisible event. Skogan (1990) analyzed that fear and neighbourhood disorder were strongly
correlated. People did not feel safe in neighbourhoods where disorder was high.
Disorder could be linked to spatial patterns of fear (Kohm, 2009). This is especially the case with
disorder that has a social nature (e.g. drunk people and drug addicts). Physical disorder does not tend
to provoke fear for most of the residents of a high-crime community, but by visitors to an area it might
be read as an invite for crime. This means that the possible contact with disorderly people concerns
the residents more than the physical disorder. In contrary to Kohm, Brunton‐Smith and Sturgis (2011)
demonstrated that visible signs of neighbourhood disorder are a highly predictive of expressed fear of
crime. This means that individuals respond to visual cues like graffiti, litter, and vandalism in the
neighbourhood and use this information in relation to their risk of being victimized. Kanan and Pruitt
(2002) also found evidence that the residents’ perceptions of physical disorder such as signs of decay
and deterioration in their neighbourhood, increased the levels of fear, the perceived risk of
victimization, while it negatively influenced the neighbourhood safety ratings of the residents. In
addition, perceptions of neighbourhood disorder made people to be more concerned with crime.
McGarrell, Giacomazzi, and Thurman (1997) examined the effects of fear of crime, neighbourhood
disorder and social integration. A finding of this research was that the more a person perceives
neighbors to be responding to noisy youths, the more this person feels rooted in the community, the
more responsive this person perceives local institutions, and the more this person perceives this
neighbourhood as a home where people help each other, the less fear this person experienced. This
does not only mean that the decline of a community and disorder facilitate fear, but that social
support, responsiveness, integration and social control inhibit fear.
The claim of the Broken Windows Theory that perceived disorder makes residents fearful is supported
by other studies like discussed above. Neighbourhood disorder does not only makes residents fearful,
but it also lowers the social control in a neighbourhood (McGarrell et al., 1997). This will be further
discussed in subparagraph 2.2.2.
2.2.2 Social control It is mentioned that perceived disorder makes residents fearful and withdraw from the community. As
a result this diminished the social control in the neighbourhood and this gives criminals the space to
operate in a neighbourhood (Wilson & Kelling, 1982). This makes social control another key part of the
Broken Windows Theory.
Perceived neighbourhood disorder is associated with the erosion of social ties (Ross & Jang, 2000).
People who live in neighbourhoods with a high level of disorder report lower levels of informal
integration with neighbors. Those people also report somewhat lower levels of formal participation in
neighbourhood organizations. A lack of informal attachments to neighbors makes the unfavorable
effects of perceived neighbourhood disorder on fear and mistrust worse (Ross & Jang, 2000). In
addition informal social ties decreases the negative effects of neighbourhood disorder. Neighbourhood
disorder plays a substantial role in weakening the local social cohesion, trust, informal social control
and local social ties with regard to elderly people (Oh, 2003). This also shows that perceived
neighbourhood disorder will subvert local trust and informal social control among residents (Oh, 2003).
17 | P a g e
In addition, elderly people in general who perceived neighbourhood disorder tend to mistrust their
neighbors and as a result do not pay attention to deviant behavior of local children. Neighbourhood
disorder has an impact on fear and informal social control which is claimed in the Broken Windows
Theory (Oh, 2003; Ross & Jang, 2000).
Ross, Mirowsky, and Pribesh (2001) studied the effects of neighbourhood disorder on the development
of mistrust among individuals, with a few resources, who live in places with a common threat and
where resources are scarce, and who feel powerless to avoid or deal with the threat. Ross, Mirowsky
and Pribesh came to the conclusion that life under threatening conditions promotes mistrust. When
people experience noise, vandalism, danger, public drinking, rundown and neglected buildings, crime,
drugs and trouble in their neighbourhood on a daily basis, they learn that it is safer not to not trust
other people (Ross et al., 2001). Residents may have come to this conclusion due to that the visible
signs of neighbourhood disorder may have indicated that the other residents of the neighbourhood
are not concerned with public order and have little or no respect for other people or their property.
Residents in these neighbourhoods view those around them with suspicion. Ross, Mirowsky and
Pribesh (2001) also found that widespread mistrust in a neighbourhood, could interfere with the ability
of residents to form ties with each other and this could further break down the order in a
neighbourhood. In addition Ross, Mirowsky and Pribesh’s analysis demonstrates that a great part of
the association between neighbourhood conditions and individual mistrust presumably represents the
impact of the social and physical environment on the perceptions of individuals of others and
themselves. The outcomes of this study shows that mistrust and perceptions of powerlessness could
weaken a neighbourhood and therefore allowing disorder to proliferate.
Neighbourhood disorder has an effect on social control. Deteriorated and decayed neighbourhoods
lead to that residents do not trust their neighbors, do not have social ties and do not correct
misbehavior.
2.3 Discussion in the literature The relationship between (neighbourhood) disorder and crime has been tested in several studies.
These studies will be discussed in this paragraph. In the literature about the broken windows there is
a discussion about if the relationship between neighbourhood disorder and crime exists.
Skogan (1990) claims to have found empirical evidence about the direct link between disorder and crime in his study Disorder and Decline: Crime and the Spiral of Decay in American Neighbourhoods. Skogan concludes that racial composition, poverty and instability of neighbourhoods are strongly connected with crime, but a substantial portion of this connection is through disorder. When disorder is taken into account this connection disappears. Kelling and Coles (1996) said that his evidence proves that crime and disorder go together in a substantial way. This is criticized by Harcourt (1998), because the evidence of Skogan misses a large number of values for important variables (e.g. harassment and litter). In addition, Skogan used for measuring disorder measures that included crime, for instance using respondents’ assessment of gang activity. This means that both the dependent and independent variable are measuring crime. Skogan also referred to the dependent variable as crime in general, but selected as dependent variable only one type of crime, namely robbery. This turned out to be the type of crime with the highest correlation with neighbourhood disorder. On the other hand Perkins et al. (1992) observed results which confirm key portions of the study of Skogan and the Broken Windows Theory. The study of Perkins et al. found that the presence of disorder-related signals engender observations of social and crime problems.
18 | P a g e
Sampson and Raudenbush (1999) also examined if disorder leads to crime and they found that disorder does not directly promote crime. Although they found that disorder and crime are related. Sampson and Raudenbush found that crime and disorder both could be explained by collective efficacy. Collective efficacy could be described as ‘the linkage of mutual trust and the willingness to intervene for the common good’ (Sampson et al., 1997). In their research they also found a reciprocal feedback loop between collective efficacy and crime. The first one who raised this issue was Skogan (1990). He stated that crime (in particular interpersonal crimes of violence) undermines the residents’ sense of control and social trust. In addition, Liska and Warner (1991) found that social interactions in public settings are constrained by robbery. This could potentially dampen social cohesion and the creation of shared expectations among residents of a neighbourhood for taking action to safeguard their community. Kelling and Coles (1996) explain in their research that social control mechanisms in communities are broken down through crime and fear of crime. Fearful citizens will stay in their homes, lock themselves behind closed doors, limit their normal associations and activities and refrain from their basic civic obligations. As citizens withdraw physically, the social controls within the community are getting relinquished, because citizens do not only withdraw to their homes, but they also withdraw from their roles of mutual support with fellow citizens. This isolates residents and undermines the solidarity and the mutual trust among neighbors. Social cohesion is out of the question when people stop interacting with one another and this is also the case for the shared willingness to involve in informal social control of the public space. The reversed influence of crime on collective efficacy is confirmed by the reciprocal feedback loop of Sampson and Raudenbush (1999). This indirectly supports the suggestion that disorder and crime are related. In figure 2 are the results of Sampson and Raudenbush’s reciprocal feedback loop summarized.
Figure 2 shows that with the confirmation of the reverse causation from crime to collective efficacy, an indirect causal relationship between disorder and crime has been established (Xu et al., 2005). This is the case due to that the fact relationship has two significant negative links (Sampson & Raudenbush, 1999). The first one is from crime to collective efficacy and the second one is from collective efficacy to disorder. This results in a positive relationship between crime and disorder that is mediated by collective efficacy (Xu et al., 2005). In accordance with this finding, Gault and Silver (2008) state that the opponents of the Broken Windows Theory did not consider this theory never stated that disorder had a direct unmediated effect on crime. The Broken Windows Theory claims that disorder undermines informal social control, which subsequently leads to an increase in crime. In addition Xu et al. (2005) found that the moral and physical decay of a community leads to increased criminality. They found that disorder has a strong direct, indirect, and total effect on crime even with collective efficacy being controlled for. However, according to Xu et al. does disorder elicit more fear of crime than actual crime.
Gau and Pratt (2008) are also critical about the Broken Windows Theory. The focus of their study was
on the distinction between crime and disorder. The Broken Windows Theory assumes that disorder
and crime are two different phenomena. Gau and Pratt tested this assumption and came to the
conclusion that citizens could not always distinguish disorder from crime. These two phenomena had
blended together in the view of the citizens. This means that when disorder is being measured, crime
Disorder
Collective efficacy Crime
Figure 2: Reciprocal feedback loop (Sampson, 2009)
19 | P a g e
will be measured as well. In that case it is tautological, because crime cannot be logically claimed to
cause itself. In later research (2010) Gau and Pratt observed that residents of disordered areas might
develop a sharper grasp on the distinction between routine nuisance and true danger. This made them
less critical about the Broken Windows Theory.
Skogan (2008) is not impressed with the critiques that disorder and crime are linked in complex ways
to race and class, because nearly everything in criminology is strongly correlated with structural
neighbourhood characteristics. In a more recent study (Boggess & Maskaly, 2014) a variety of factors
with regard to social disorganization or in other words socioeconomic factors are controlled for, when
testing the relationship between disorder and crime. The results showed that higher rates of disorder
lead to significant, although modest, increases of violent crime.
As discussed in this paragraph, there is disagreement about the empirical relationship between
neighbourhood disorder and crime. The evidence for neighbourhood disorder causing crime was
criticized; other researchers could not find this evidence. However, it is observed that disorder and
crime are correlated.
2.4 Hypotheses In this section are hypotheses formulated that will help to answer the research questions. The review
of the literature shows that neighbourhood disorder and crime are connected to each other. Several
authors stated that there is a relationship between disorder and crime. Where Skogan (1990) claimed
that neighbourhood disorder is a cause of crime, do other authors (Harcourt, 1998, 2001; Sampson &
Raudenbush, 2004) refute this claim. From the discussion about the Broken Windows Theory could be
derived that neighbourhood disorder and crime are correlated. This leads to the first hypotheses of
this research:
The expectation is that neighbourhood disorder and crime will be correlated. This means that
neighbourhoods with a high level of disorder will have a high level of crime and neighbourhoods with
a low level of disorder will have a low level of crime.
The Broken Windows Theory connects disorder to crime. The theory states that when disorder
increases in a neighbourhood, crime increases as well. Although the theory does not state that disorder
directly causes crime to increase. The Broken Windows Theory states that in neighbourhoods were
disorder is increasing; this lowers the social control which gives room to criminals to operate in these
neighbourhoods. According to Gault and Silver (2008) undermines disorder social control, which
subsequently leads to an increase in crime. Whereas Xu et al. (2005) found a strong direct, indirect,
and total effect on crime even with collective efficacy being controlled for. The theoretical framework
showed several relations between neighbourhood disorder, fear of crime, social control/collective
efficacy and crime. These relationships are shown in figure 3.
Hypothesis 1: Neighbourhoods with a higher level of neighbourhood disorder will have a higher
level of crime, than neighbourhoods with a lower level of neighbourhood disorder.
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Disorder Fear of crime Social controlDisorder
Crime
Social controlFear of crime
-
+ -
+
+ -
Figure 3: Relations between the variables of the Broken Windows Theory
The relationships that are shown in figure 3 will be examined in this research. The variables fear of
crime and social control are included in figure 3 to see if these variables enhance the relationship
between neighbourhood disorder and crime. This leads to the following hypotheses.
The hypotheses formulated above are the relations which are shown in figure 3, except for the
relationship between neighbourhood disorder and crime. This relationship is already formulated in
hypothesis 1
2.5 Conclusion This paragraph will answer the following research question: What is neighbourhood disorder and what is theoretically and empirically known about the impact of neighbourhood disorder on crime?
The definition of Skogan (1990) refers to neighbourhood disorder as visible indications of a lack of
order and social control in the community. Two components could be distinguished: social and physical
disorder. Social disorder could be seen as signs that indicate a lack of social control that involve people
and physical disorder as the entire physical appearance and decay of a neighbourhood (Ross &
Mirowsky, 1999). In the literature there is the question if crime and disorder are two different concepts
or that they are basically at opposite ends of the same continuum (Sampson & Raudenbush, 1999).
Hypothesis 2: Neighbourhoods with a higher level of disorder will have a higher level of fear of
crime, than neighbourhoods with a lower level of disorder.
Hypothesis 3: Neighbourhoods with a higher level of disorder will have a lower degree of social
control than neighbourhoods with a lower level of disorder.
Hypothesis 4: Neighbourhoods with a higher level of fear of crime will have a lower degree of
social control than neighbourhoods with a lower level of fear of crime.
Hypothesis 5: Neighbourhoods with a higher level of fear of crime will have a higher level of crime,
than neighbourhoods with a lower level of fear of crime.
Hypotheses 6: Neighbourhoods with a lower degree of social control will have a higher level of
crime than neighbourhoods with a high degree of social control.
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A theory which addresses the relationship between neighbourhood disorder and crime is the Broken
Windows Theory. The Broken Windows Theory suggests that minor forms of public disorder (e.g.
broken windows) could lead to severe crime and a downward spiral of urban decay. Wilson and Kelling
(1982) suggest ‘that untended behavior leads to the breakdown of community controls’. Perceived
disorder will lead to that inhabitants think that crime is on the rise and therefore they will modify their
behavior. These inhabitants will retreat to their homes, become less likely to intervene against
disorderly people and in some cases this could lead to that ‘good’ inhabitants move away. This results
in a lowering of informal social control, which could lead to more disorder and crime, because people
see that in these areas they can get away unpunished with such behavior. Eventually criminals could
see such a neighbourhood as a good place to work with impunity.
In the literature about the Broken Windows Theory is disagreement about the empirical relationship
between neighbourhood disorder and crime. The evidence indicating that neighbourhood disorder
causes crime was criticized and other researchers could not find this evidence. However, it is observed
that disorder and crime are correlated and that disorder has a strong direct, indirect, and total effect
on crime even with collective efficacy being controlled for (Xu et al., 2005).
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3. Data and operationalization
This chapter will describe how this research was conducted. The units of analysis will be described in
paragraph 3.1. Subsequently the operationalization of the variables will be elaborated in paragraph
3.2. Important variables for this research are crime and neighbourhood disorder, but also fear of crime
and social control. Finally, the way in which this research is conducted will be explained in paragraph
3.3. This paragraph provides information about the data collection and the statistical analysis.
3.1 Units of analysis This paragraph will discuss the units of analysis of this research. The units of analysis are districts of
Rotterdam. There are fourteen districts of Rotterdam, but in this research only thirteen will be used.
The reason for this is that the district Rozenburg had merged with Rotterdam in 2010. This means that
there is not enough data known about the examined period (2006-2011) of this research. The normal
districts of Rotterdam, in which citizens live, are used and also their boundaries. This means that the
port and industrial areas of Rotterdam are not taken into account in this research. Figure 4 gives an
overview of the districts of Rotterdam that are used and where they are situated.
Figure 4: Overview the districts of Rotterdam (Gemeente Rotterdam, 2014).
A small description of the districts is given in appendix A. As a consequence of the exclusion of some
districts and areas, the total of 63 neighbourhoods are taken into account during the analysis of this
research. The phenomenon of interest in these neighbourhoods is the social and physical disorder
within its purview. Urban neighbourhoods are often subjectively defined entities with ambiguous
boundaries and within neighbourhoods many smaller geographical areas can be distinguished. These
geographical areas have their own specific characteristics, for example they could have different levels
of physical and social characteristics. This means that disorder and crime are measured on the
neighbourhood level, while disorder and crime are more common in specific places of
neighbourhoods, the so called ‘hotspots‘, than in the whole neighbourhood. This means that
statements that are made in this thesis are based on average crime and disorder levels in the
neighbourhood. The choice to analyze crime and disorder on the neighbourhood level is a deliberate
one. Smaller geographical areas such as ‘hotspot’s have a small amount of citizens. As a result there is
not much information available about these geographical areas. In addition, this limited information
makes it difficult to draw conclusions without strong influences of extreme observations and biases.
Furthermore, the data that are used in this thesis were only available on the neighbourhood level or
23 | P a g e
on a higher aggregation level. A higher aggregation level makes the comparison of neighbourhoods
less complex.
3.2 Operationalization In this paragraph the variables that are used and measured in this research are described and
operationalized. First the dependent variable crime will be described. Thereafter, the independent
variables that affect crime, such as neighbourhood disorder, social control and fear of crime will be
described.
3.2.1 Crime Crime is the dependent variable in this research. This means that crime can be explained with the data
about other variables such as neighbourhood disorder, fear of crime and social control. This research
is about crime in neighbourhoods and therefore not all the types of crime are included in the analysis.
The main focus of this research lies on the impact of disorder on crime in neighbourhoods. In particular
the focus lies on the consequences of disorder on the behavior of people. For example, do residents
of a neighbourhood retreat to their homes, which gives room for criminals to operate in that
neighbourhood. This means that several types of crime such as traffic and environmental
crimes/offenses are excluded from the analysis. The types of crime that are included in this research
are theft and burglary crimes, destruction and damage against public law and order crimes, violence
and sex crimes, and drugs and alcohol offenses. These types of crime are based on the literature (Gau
& Pratt, 2008; Weisburd et al., 2010) and are classified according to the Standard Classification Criminal
Offenses of the Police 2010. The different criminal offenses that are underlying the types of crime are
shown in Appendix B.
The data to measure the types of crime are acquired from the Neighbourhood Monitor Rotterdam.
This monitor contains a lot of information about multiple topics including crime. The data about crime
are registered reports from the police. This means that crime is going to be measured through the
reports of criminal offenses that are known by the police. These police reports contain all the criminal
offenses that are reported to the police between 2006 and 2011. This type of data chosen to be used
is due to the availability and the impression it gives of the total crime that is known by the police. The
most important reason to choose police reports is that these reports are not about feelings and/or
attitudes of citizens towards crime. Why this is important will be explained in subparagraph 3.2.2.
3.2.2 Neighbourhood disorder The most important independent variable of this research is neighbourhood disorder. After all, this
research is about the impact of neighbourhood disorder on crime. The theoretical framework provided
an explanation about the concept of disorder. It explained that two components could be
distinguished: social and physical. In order to answer the central research question, disorder will be
analyzed as a whole. Although it could be interesting for policy makers and recommendations to know
if one of the two components has a bigger impact on crime. Therefore is the impact of social and
physical disorder on crime also examined. Neighbourhood disorder is measured by questions about
indicators regarding nuisance, vandalism and the physical environment of the neighbourhood. These
questions show the attitudes and/or feelings of citizens towards the mentioned topics. The indicators
of neighbourhood disorder that are used in this research are shown on page 25. Additional information
about these indicators is available in appendix B.
24 | P a g e
Social neighbourhood disorder indicators:
1. Drugs nuisance 2. Loitering 3. Drunk people on the streets 4. Harassment on the streets
Physical neighbourhood disorder indicators:
1. Graffiti on walls and buildings 2. Destruction of phone booths, bus- and tram stops 3. Litter next to the container 4. Urinating in public 5. Holes or cracks in pavement 6. Dog dirt on the streets 7. Pollution on the streets 8. Broken or destroyed street furniture
The indicators are based upon other studies (Chappell et al., 2011; Gau & Pratt, 2008; Ross & Mirowsky,
1999) and the theory of the broken window theory. The indicators of neighbourhood disorder are all
in terms of the number of citizens who thinks that a certain issue, for example graffiti, is a
neighbourhood problem. This means that all the indicators give a score between 0 – 100. Important to
mention is that neighbourhood disorder is measured with a subjective measure and that crime is
measured with an objective measure. In the literature is mentioned that disorder and crime are at
opposite ends of the same continuum (Sampson & Raudenbush, 1999). This means that it could be
possible that crime is also measured when disorder is being measured. To avoid that crime and
disorder are measured by the same indicators, there is chosen to measure neighbourhood disorder as
the feelings and/or attitudes of citizens and crime as police reports. Feelings and attitudes of citizens
of Rotterdam towards the above standing indicators are subjective and could therefore not be seen as
crime. This ensures that there is a clear distinction between the measures of the two variables.
3.2.3 Fear of crime The fear of crime of citizens is based on the percentage of residents that feel themselves unsafe in
their own neighbourhood. This is measured using data of the Safety Monitor. The particular question
of the Safety Monitor was: “Do you feel unsafe in your own neighbourhood”? The percentage of the
citizens that felt unsafe answered ‘yes’ on this question. This means that the number of persons who
answered this question is based on the number of participants and is not based on the ‘real’ number
of citizens. Although, the Safety Monitor uses at least 30% of the residents in a neighbourhood. This
ensures that the reliability and validity of the conclusions that are drawn from this data, can be
maintained.
The fear of crime is a negative variable, because it causes people to retreat to their homes according
to the Broken Window Theory. In addition, according to the Broken Window Theory does fear of crime
increase when disorder increases in a neighbourhood.
3.2.4 Social control In the theoretical framework were some concepts of social control mentioned. Important concepts
regarding social control are social ties between citizens, participation of citizens in the society and trust
25 | P a g e
among citizens (van Stokkom & Toenders, 2010). In this research social control is composed of the
following three indicators:
1. The participation of citizens
2. The social ties of citizens
3. Satisfaction with the neighbourhood
The underlying factors of which the indicators are composes are shown in appendix B. The level of
social control within the neighbourhood will be measured by a scale score (0-100). This score is based
on the indicators that are mentioned. The indicators that are used to measure social control are based
on previous studies (Beckett & Herbert, 2008; Bellair, 2000). Underlying these indicators are several
questions from the Social Index of Rotterdam and data from other sources such as the municipal basic
administration. Social control is a positive variable. Social control normally has a positive effect on a
neighbourhood (van Stokkom & Toenders, 2010).
3.3 Research design This paragraph describes the research method that is used for conducting this research. To conduct
this research several methods have been used. The first method was desk research. This was used to
get acquainted with the concept of disorder and to formulate hypotheses. To test the hypotheses data
have been collected from multiple sources. These sources will be explained in sub-paragraph 3.3.1 and
the manner in which the hypothesis is going to be tested will be discussed in sub-paragraph 3.3.2. This
research is about the impact of neighbourhood disorder on crime in the districts of Rotterdam between
2006 and 2011. The cross sectional design is chosen for this research, because there are only data
available over a period of six years. A cross sectional design involves observations of a population, or
cross section, or phenomenon or a sample at one point in time (Babbie, 2010). This means that the
averages of the available data about the period 2006-2011 is calculated.
3.3.1 Data collection The data that are collected to perform the analysis are existing data originated from multiple sources.
The data to measure crime is acquired from the Neighbourhood Monitor Rotterdam. This monitor
contains a lot of information about multiple topics including crime. The data about crime are registered
reports from the police. This monitor is also used to get data about the structural characteristics of the
districts of Rotterdam.
The source that mainly used for collecting data about neighbourhood disorder is the Neighbourhood
Profile. The Neighbourhood Profile is a monitor instrument of the municipality of Rotterdam used to
create a coherent image of the actual and the subjective situation experienced through citizens about
the safety, and the social and physical state of the neighbourhoods. This instrument makes it possible
to compare neighbourhoods among each other and in the time. The data are gathered through surveys
which are randomly held among approximately 30.000 citizens. Because of that the same approach is
used among all the districts of Rotterdam, the data are usable to draw conclusions that combine all
the districts. This will increase the reliability and the validity of this research.
The data used to collect data about fear of crime are originative of the Safety Monitor. This is an annual
research that is being held among municipalities in the Netherlands based on livability, safety and
victimization. The Safety Monitor gathered their data from questionnaires among citizens of
Rotterdam. This data is gathered at the district and neighbourhood level between 2006 and 2011. The
26 | P a g e
citizens who completed the questionnaire were asked about their opinion regarding police functioning,
livability, unsafe spots in their neighbourhood, perceived safety and nuisance in the neighbourhood.
The data from the Safety Monitor that will be used are the number of citizens in Rotterdam who feel
themselves unsafe in their own neighbourhood.
The source used to get data about social control in the districts of Rotterdam is the Social Index of
Rotterdam. This index gives information about participation of citizens in the society, social ties and
the involvement of citizens with their neighbourhood. This data is also gathered with surveys among
citizens of Rotterdam at the district and neighbourhood level between 2008 and 2012.
3.3.2 Statistical analysis In this section are the methods for testing of the hypotheses explained. In the theoretical framework
six hypotheses are formulated to test the relationships between neighbourhood disorder, fear of
crime, social control and crime.
The hypotheses based on the theoretical framework are tested with the statistical software program
‘SPSS’. The variables used for the analysis are measured at the ratio-level, which makes the correlation
or regression analyses most suitable to perform the analyses (Babbie, 2010). All The hypotheses
suggest a relation between two variables and due to the cross sectional design of this research,
correlations are used to test the hypotheses. With the cross sectional design that is used in this
research, it is not possible to establish a causal link between the tested variables. Another thing that
has to be kept in mind is that there are many factors involved in explaining crime rates and therefore
the omitted variable bias will most likely affect the analysis. This is another reason to describe the
relations between the examined variables in terms of correlation and not causation.
When the correlation is measured, this means that the direction and strength of a linear association
between two variables is measured (Deveaux, Velleman, & Bock, 2011). Three conditions are in order
when correlational analysis is used. The quantitative variable condition, the outlier condition and the
straight enough condition. It could be possible that in some cases it may not be able to comply with
the outlier and the straight enough condition, when using data about neighbourhood disorder and
crime rates. Because of the differences between the neighbourhoods in Rotterdam it is possible that
an outlier could affect the relationship between two variables. It could also be the case that the
relationship between two variables is not linear. To deal with these problems the nonparametric
measure Spearman’s rho is used. This measure replaces the original data with their ranks within each
variable. This method has the advantage over the Pearson correlation coefficient that this measure
can still be used if only the ranks are known. In addition, the replacement of the original values with
their ranks ensures that this measure is not much affected by outliers. The Spearman’s rho measure
does not make the assumption that a trend is linear, this fixes problems with the straight enough
condition. The disadvantage of this measure is that it is not applicable for more complex methods. The
level of significance that will be used in this research is p = 0.05, which tells us that the results of the
statistical analyses are for 95% a reflection of the reality. When the outcomes of the statistical
regression analyses do not meet the level of significance it will be difficult to draw conclusions based
on these results, because it is most likely that the results are not reliable.
Table 1 gives an overview of the variables that are used in this research. These variables were already
explained in section 3.2. The variables are presented with their minimum, maximum and mean values
in table 1. The formula of how a variable is computed with regard to their underlying indicators is also
presented. The Cronbach’s alpha is given to show if the indicators are allowed to be measured as one
27 | P a g e
scale. Finally, table 1 shows which source is used to acquire the data that is used to measure the
variables. Table 1 shows crime as a whole and the types of crime that are measured in this research.
The reason for this is that the Cronbach’s alpha score of crime with the four types of crime being pooled
is too low to use them as one scale of crime. The rule of thumb with regard to the score of the
Cronbach’s alpha is that indicators could be merged to one scale when the score is at least ,700. As
table 1 shows, the score of the Cronbach’s alpha of the four types of crime is ,537. This means that in
the analyses of this research the four types of crime will be separately measured. Table 1 shows also
variables without a formula or Cronbach’s alpha score. Such variables are measured with one indicator
and this means that a formula or Cronbach’s alpha score is not applicable. The values that are shown
in the min., max., and mean columns are absolute values and percentages. The absolute values are
applicable on crime and the types of crime. The values about crime are reported crimes to the police.
The values of the other variables are percentages.
Variable Min. Max. Mean Std. dev. Formula Cronbach’s
Alpha Source
Crime 56,0 4427,0 841,92 755,83 The sum of the crime indicators
,537 Neighbourhood monitor (Police data)
Theft and burglary crimes
30,0 3341,0 574,43 567,21 - - Neighbourhood monitor (Police data)
Destruction or material damage
8 483 133,68 93,41 - - Neighbourhood monitor (Police data)
Violence and sex crimes
8 555 118,95 101,97 - - Neighbourhood monitor (Police data)
Drugs and alcohol offenses
1 98 14,87 17,65 - - Neighbourhood monitor (Police data)
Neighbourhood disorder
5,97 26,86 17,87 5,47 (The sum of all the disorder indicators) / 12
,876 Neighbourhood profile
Physical disorder 7,91 31,04 22,04 5,92 (The sum of the physical disorder indicators) /8
,779 Neighbourhood profile
Social disorder 1,30 22,65 9,53 5,12 (The sum of the social disorder indicators) / 4
,907 Neighbourhood profile
Fear of crime 13,05 43,45 30,02 6,91 - - Safety Monitor
Social control 52,67 81,87 65,91 7,32 (Participation + social ties + satisfaction with the neighbourhood )/ 3
,772 Social Index of Rotterdam
Table 1: Variables that are used in this research (N=63)
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4. Variation and development of crime in Rotterdam between 2006
and 2011
This chapter describes the variation and developments of crime in the districts of Rotterdam during the period 2006-2011. It addresses the second research question: What is the variation in crime rates and how have the crime rates developed in the districts of Rotterdam between 2006 and 2011? Four types of crime are selected for this research. Underlying these types of crime are criminal offenses. Important statistics about the development and variation in crime rates are given in this chapter to provide for an answer to the research question. In order to provide an answer, a closer look will be taken into the development of crime, the magnitude of the decline in crime rates, the fluctuations during the period 2006-2011, and how broad the crime declined from a geographical point of view. The development of crime will be discussed in section 4.1. The magnitude of the crime decline will be discussed in section 4.2. Subsequently, section 4.3 is about the fluctuations in the crime decline. Section 4.4 is about the broadness of the crime decline seen from the perspective of the districts of Rotterdam. Finally, in section 4.5 will the research question be answered and this section will contain the conclusion of this chapter.
4.1 Development of crime in Rotterdam In the introduction of this research was mentioned that safety got the priority in the municipal policy of Rotterdam after Pim Fortuyn won the elections with Leefbaar Rotterdam in 2001. Another thing that was mentioned is that the safety scores improved every year after 2001. The question is if this could also been seen in the crime rates reported and registered by the police in Rotterdam. Figure 5 shows the development of the reported crime in Rotterdam in the period 2006-2011. From this figure can be derived that the crime rates declined. Although, the decline in crime rates was not consecutive in the years examined in this research. The crime rates increased in the years 2008 and 2011. The crime rates declined with 10,1 percent in Rotterdam in this period.
The crime rates declined in the period 2006-2011, but how was the distribution among the districts of Rotterdam? Did some districts contribute more to the number of reported crime than other districts? Figure 6 shows the distribution of the crime rates among the districts of Rotterdam in the period 2006-2011. This figure shows that Stadscentrum is the district with the most reported crime. The districts that follow with the subsequent highest reported crime are Prins Alexander, Charlois, Delfshaven and Feijenoord. The districts that have the lowest number of reported crime are Pernis, Hoek van Holland and Overschie. Hillegersberg-Schiebroek and Hoogvliet are other districts that have a low number of reported crime. Almost all the districts have a similar development as the total number of crime. However, some of the districts that have a larger number of crime do not follow the exact same development of the total crime. The differences are not big and concern mostly one year that does not
40000
45000
50000
55000
60000
2005 2006 2007 2008 2009 2010 2011 2012
Figure 5: Development of crime since 2006, Rotterdam, 2011. Source: (processed data from) Police Rotterdam.
29 | P a g e
develop in the same direction as the total crime. For example, the district Stadscentrum follows the same development as the total crime except for the year 2007, in which the crime rates increased whereas the crime rates of the total crime declined. The distribution of crime is consistent with a previous research that is conducted in the Netherlands (Bernasco, 2011). This research stated that the most crimes are committed in the city center or the adjacent neighbourhoods. The only neighbourhood that is an exception to this statement is Prins Alexander, which is situated at the outskirts of Rotterdam.
Figure 6: Development of crime in the districts since 2006, Rotterdam, 2011. Source: (processed data from) Police Rotterdam
4.2 Magnitude of the crime decline This section is about the magnitude of the crime decline in Rotterdam between 2006 and 2011. The
magnitude of the crime decline is determined by comparing the most recent crime levels that are being
used in this research against the highest point of crime rates in Rotterdam in this period (Zimring,
2012). This means that the highest levels of crime rates of the types of crime will be compared with
the level of crime in 2011. Another method to measure the magnitude of the crime decline is to
compare the lowest rate of crime with the highest rate of crime. The outcomes of this method could
give information about the consistency and length of the crime decline in Rotterdam. This will be
discussed in a more extensive way in section 4.2. Figure 7 shows both the methods of measuring the
magnitude of the decline of crime.
The first method of determining the magnitude shows that crime declined with rates of 8,4 percent to
15,8 percent between 2006 and 2011. The types of crime that declined were theft and burglary crimes,
destruction and damage against public law and order crimes and, violence- and sex crimes. Remarkably
is that drugs and alcohol offenses did not decline but increased with 2,4 percent in this period. When
looking at the lowest rates compared to the highest rates it is striking that the percentages of the
decline are higher. Even drugs and alcohol did decline according to this method. This indicates that, in
the case of drugs and alcohol offenses, the highest rates were registered in the year 2011. This method
also indicates that 2011 is not the year with the least recorded crimes. In addition, the higher scores
also indicate that the decline in crime was not very consistent and that the crime rates fluctuated in
the period 2006-2011.
0
2000
4000
6000
8000
10000
12000
2006 2007 2008 2009 2010 2011
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Figure 7: Magnitude of the crime decline since 2006, Rotterdam, 2011. Source: (processed data from) Police Rotterdam
4.2 Fluctuations in the crime decline The second aspect of the crime decline in Rotterdam that will be discussed are the fluctuations in the
crime decline between 2006 and 2011. This section describes the trends of the four types of crime
separately. Figure 8 shows the development of the different types of crime in this period. The number
of crime is shown as the total crime that is reported by the police. Theft and burglary crimes is the
most reported type of crime. This is consistent with the national developments of crime (Akkermans
& van Rosmalen, 2012). These developments show that property crimes are the most frequent in the
Netherlands, followed by vandalism offenses and then violence- and sex crimes. Drugs and alcohol
offenses are a far less common crime in the Netherlands.
8,410,3
14,1
-2,4
13,5
40,1
14,6
19,7
Theft andburglary
Destructionand damage
against publiclaw and order
Violence- andsex crimes
Drugs andalcohol
offenses
Highestpointcomparedto mostrecentpoint ofcrime
Highestrate vs.lowest rate
Figure 8: Fluctuations in the crime decline since 2006, Rotterdam, 2011. Source: (processed data from) Police Rotterdam
31 | P a g e
When looking to the development of the types of crime in the period 2006-2011, it is striking that not
one of the trend lines is approximately the same. In addition, not a single type of crime has a
consecutive decline in this period. As a matter of fact all the trend lines fluctuate. Violence and sex
crimes has the most consistent crime decline. Noteworthy to mention is that all the types of crime that
were declining in 2010 have been more reported in 2011. Almost all the types of crime were less
reported, except for drugs and alcohol offenses in 2011 than in 2006.
4.3 Extensiveness of the crime decline This section describes the crime decline in Rotterdam by including the thirteen districts. This leads to the question of how extensive the crime decline in Rotterdam was seen from a geographical point of view during the period 2006-2011. Meaning that the development of the four types of crimes in the thirteen districts of Rotterdam are taken into account. Differences and similarities among the trends in crime can be discussed based on the outcomes. Figure 9 shows the trend for the types of crime in the thirteen districts of Rotterdam during the period 2006-2011. The districts differ in size, number of inhabitants and composition, which could affect the number of crime in a district. Index numbers are used in figure 9 to prevent that one of those or other factors influence the numbers. These numbers will provide for the opportunity to discuss similarities and differences in the crime decline in the districts of Rotterdam. Trends in theft and burglary crimes The trends that were observed in the previous paragraphs of this chapter are also observable for theft and burglary crimes in all the city districts. For almost all the districts counts that there was a significant crime decline between 2006 and 2007. Subsequently, crime was more reported in almost all the city districts in the period 2007-2009. The crime rates declined after this period until the year 2010 whereupon the crime was again more reported in almost all the city districts, except for Charlois. Striking was the development of the theft and burglary crimes in the districts Hoek van Holland and Pernis. The development of Hoek van Holland is striking due to the strong fluctuations between 2006 and 2011. The development of Pernis is noteworthy, because theft and burglary crimes strongly increased in this district. This could possibly be explained due to the fact that Pernis and Hoek van Holland are districts with a relative low number of citizens and a low number of reported crimes between 2006 and 2011. Therefore have the changes in crime rates a more significant effect than in districts with a larger number of reported crimes. The district that had the strongest decline is Kralingen-Crooswijk (23%), followed by Stadscentrum (20%), Noord (19%), Charlois (17%), Delfshaven (11%), Feijenoord (11%), Hoek van Holland (8%), IJsselmonde (2%) and Overschie (1%). There was no crime decline in the remaining districts. Trends in destruction and damage against public law and order The crime decline of destruction and damage against public law and order developed are relatively similar in the districts of Rotterdam. In the period 2006-2009, the development of the destruction and damage against public law and order crimes fluctuated approximately between index scores of 80 and 120. There are three outliers, namely Hoek van Holland (156) in a negative manner and Overschie (80) and Kralingen-Crooswijk (80) in a positive manner. The crime rates in all the city districts declined after 2009, whereupon it increased in 2011. In this year, the index scores of almost all the districts are lower than the scores in 2006, except for the score of Hoek van Holland (103). The district that had the strongest decline is Kralingen-Crooswijk (37%) followed by Hillegersberg-Schiebroek (28%), Overschie (24%), Stadscentrum (13%), Feijenoord (11%), Noord (10%), Prins Alexander (9%), Charlois (7%), IJsselmonde (5%), Pernis (5%), Delfshaven (3%) and Hoogvliet (3%). Trends in violence and sex crimes The trends of violence and sex crimes showed a slight decline in almost all the districts of Rotterdam. Striking is the development of the violence and sex crimes in the districts of Hoogvliet, Hoek van
32 | P a g e
Holland and Pernis. The development in these districts differs from the development in the other districts. Hoek van Holland showed an increase in the year 2007, whereupon it decreased to an index score below 80 in 2008. Thereafter crime increases to index scores around or above 140. The reported violence and sex crimes in Hoogvliet increased 46 percent in the period 2006-2008. After this period, the violence and sex crimes reports dropped sharply. This resulted in that the reports of violence and sex crimes were eleven percent less reported in 2011 than in 2006. The development of violence and sex crimes in Pernis is striking, because it differed from most districts due to that it had lower index scores in the period 2006-2008. After that period it developed in approximately the same way as most of the other districts. The district that had the strongest decline is Overschie (37%) followed by Delfshaven (21%), Hillegersberg-Schiebroek (21%), Noord (19%), Charlois (18%), Stadscentrum (17%), Kralingen-Croosbeek (16%), Hoogvliet (11%), Prins Alexander (11%), Feijenoord (10%) and Pernis (9%). Trends in drugs and alcohol offenses The trends in drugs and alcohol offenses are more extreme than the trends in the other discussed types of crime. The reason for the extreme differences is the fact that drugs and alcohol offenses are way less reported than the other types of crime. Therefore changes in the number of reports were relatively more extreme. In some of the districts were drugs and alcohol offenses less than ten times reported in a year. Examples of those districts are Pernis, Hoogvliet and Overschie. This can also be seen in figure 9. The two districts with the most extreme fluctuation are Pernis and Overschie. In paragraph 4.1 came forward that drugs and alcohol offenses increased in the period 2006-2011. This can also been seen in figure 9. The crime rates regarding drugs and alcohol offenses increased in almost every district. The districts where a decline was observed are Hoek van Holland, Hoogvliet, Hillegersberg-Schiebroek, Pernis and Stadscentrum.
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Figure 9: Trends in the different types of crime since 2006, Rotterdam, 2011. Source: (processed data from) Police Rotterdam
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4.4 Conclusion This chapter addressed the question: What is the variation in crime rates and how have the crime rates
developed in the districts of Rotterdam between 2006 and 2011. The crime rates declined with 10,1
percent in Rotterdam in the period 2006-2011. The development of crime fluctuated in the period
2006-2011, but the crime rates were lower in 2011 than in 2006. The district with the most reported
crime was Stadscentrum. The adjacent districts and Prins Alexander were districts with a higher
number of reported crime than the districts that were farther situated from the city center. Another
indication of the results is that theft and burglary crimes is the most reported crime of the four types
that are examined in this research. This type of crime was even on its lowest point way more reported
than the other types of crime in the period 2006-2011. The second most reported crime was
destruction and damage against public law and order crimes followed by violence and sex crimes, and
drugs and alcohol offenses. The last mentioned type of crime showed to be way less reported than the
other types of crime.
The crime rates declined between 2006 and 2011. Although there was a difference in the magnitude
of the crime decline. Violence and sex crimes declined the most of the types of crime that were
distinguished in this research. The second most declined type of crime was destruction and damage
against public law and order followed by theft and burglary crimes. Drugs and alcohol offenses
increased between 2006 and 2011. The decline in crime was not consistent and fluctuated for the types
of crime. The development of the types of crime in the districts of Rotterdam showed a different
pattern per type of crime.
The most consistent type of crime was destruction and damage against public law and order in the city
districts. This type of crime had the smallest range of the decline (in percentage points). The range of
the crime decline for destruction and damage against public law and order was of 31 percent, followed
by theft and burglary crimes (66 percent), violence and sex crimes (78 percent) and drugs and alcohol
offenses increased with a range of 250 percent. The large range of drugs and alcohol offenses could
be explained by the low number of reports regarding this crime. Therefore, the differences are more
significant than changes in the number of the other types of crimes.
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5. Variation and development of neighbourhood disorder in
Rotterdam between 2006 and 2011
This chapter describes the variation and development of neighbourhood disorder in the districts of
Rotterdam during the period 2006-2011. It addresses the third research question: What is the variation
in neighbourhood disorder and how has neighbourhood disorder developed in the districts of
Rotterdam between 2006 and 2011? Worth mentioning is that there is no data about the year 2010.
No surveys were held for the Neighbourhood Profile among the citizens of Rotterdam in that year.
Important statistics about the development and variation in neighbourhood disorder will be given in
this chapter to provide for an answer for the above mentioned research question. In order to provide
for an answer a closer look will be taken into the development of neighbourhood disorder, social
disorder and physical disorder. The development of neighbourhood disorder will be discussed in
section 5.1. Section 5.2 describes the development of social disorder and the development of physical
disorder will be described in section 5.3. Section 5.4 describes the magnitude of the change in disorder
in the districts of Rotterdam. Finally, the research question will be answered in section 5.5 and this
section will contain the conclusion of this chapter.
5.1 Development of neighbourhood disorder in Rotterdam When safety got the priority in the municipal policy of Rotterdam in 2001, one of the targets of that
policy was to make Rotterdam cleaner and more undamaged. Neighbourhood disorder is measured as
the percentage of the citizens that thought that disorder was a problem in their neighbourhood. The
development of the percentage of the citizens that perceived neighbourhood disorder to be a problem
in their neighbourhood is shown in figure 10.
Figure 10: Development of disorder since 2006, Rotterdam, 2011. Source: (processed data from) Directie Veiligheid Rotterdam
Figure 10 shows that the perceived disorder fluctuated in Rotterdam between 2006 and 2011. From
2006 to 2007 disorder in the neighbourhood was slightly less perceived to be a problem. The citizens
of Rotterdam who perceived disorder as a neighbourhood problem increased in 2008. In the period
2009-2011, disorder in neighbourhoods were steadily less perceived to be a problem per year. 15,5
percent of the citizens of Rotterdam thought that neighbourhood disorder was a problem in 2006. This
percentage increased to 17,6 percent in 2011. Meaning that the perceived neighbourhood disorder
increased between 2006 and 2011.
Figure 11 shows the development of neighbourhood disorder in the districts of Rotterdam. The
districts Charlois, Delfshaven, Feijenoord and Stadscentrum seems to be districts in which disorder is
0,0
5,0
10,0
15,0
20,0
25,0
2005 2006 2007 2008 2009 2010 2011 2012
36 | P a g e
relatively more perceived than in the other districts. It could be seen as striking, that these districts
also showed to be districts with relatively more reported crime. The average number of citizens who
perceived neighbourhood disorder in Rotterdam was 17,4 percent in the period 2006-2011. Kralingen-
Crooswijk and Noord are other districts where disorder was more perceived than the average.
Noteworthy, these districts are adjacent to the earlier mentioned districts. Just as in the case of crime
rates, the districts that are situated adjacent to the city center are districts with a higher level of
disorder. A cause for this observation could be the higher immigrant concentration, concentrated
disadvantage and population density in these districts. In the theoretical framework was mentioned
that structural characteristics matter in relation to neighbourhood disorder (Sampson & Raudenbush,
1999). The mentioned structural characteristics were found to be correlated with neighbourhood
disorder.
In the remaining districts disorder was the least perceived to be a neighbourhood problem. These
districts are situated at the outskirts of Rotterdam. In addition, these districts have the lowest number
of citizens, except for Prins Alexander which is the district with the largest number of citizens in
Rotterdam. Stadscentrum was the district with the most perceived disorder in 2006 and Delfshaven is
the district with the most perceived disorder in 2011.
Figure 11: Development of disorder in the districts since 2006, Rotterdam, 2011. Source: (processed data from) Directie Veiligheid Rotterdam
5.2 Development of social disorder in Rotterdam This section will take a closer look into the development of social disorder in Rotterdam. The indicators
for social disorder are drug nuisance, loitering, drunken people and harassment of people who are
passing by. Social disorder in neighbourhoods is related to human behavior. This means that it is
perceived to be a neighbourhood problem when a resident sees somebody committing one of the
above mentioned actions and the consequences of these actions (e.g. drugs needles). As a result, social
disorder is less perceived to be a neighbourhood problem than the general level of disorder that is
perceived in a neighbourhood. Social disorder has often a temporary nature, while physical disorder is
visible until it is fixed or removed, such as graffiti on the walls of a building or litter next to the
container. Figure 12 shows the development of social disorder in Rotterdam.
0,0
5,0
10,0
15,0
20,0
25,0
30,0
2006 2007 2008 2009 2011
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Figure 12: Development of social disorder since 2006, Rotterdam, 2011. Source: (processed data from) Directie Veiligheid Rotterdam
The percentages of citizens that perceived social disorder to be a neighbourhood problem varied
between 6.8 percent and 10,9 percent in the period 2006-2011. Social disorder was less perceived to
be a neighbourhood problem from 2006 to 2007. It increased to percentages that fluctuated between
10,3 percent and 10,9 percent in the years after 2007. This means that the perceived social disorder
has increased in the period 2006-2011.
The development of the indicators of social disorder in Rotterdam is presented in figure 13. When
taking a closer look into the development of the indicators of social disorder, it is clear that loitering is
the most perceived as a neighbourhood problem. Drunken people in the streets is second most
perceived as a neighbourhood problem followed by drug nuisance and the harassment of people in
the streets. All the indicators are increasingly perceived to be a neighbourhood problem, except for
drug nuisance in the period 2006-2011. Drug nuisance was less perceived in 2011, where the other
indicators were more perceived to be a neighbourhood problem. However, the percentage of citizens
who thought that drugs nuisance is a neighbourhood problem is still higher in 2011 than in 2006.
Figure 13: Development of indicators of social disorder since 2006, Rotterdam, 2011. Source: (processed data from) Directie Veiligheid Rotterdam
5.3 Development of physical disorder in Rotterdam This section describes the development of physical disorder in Rotterdam. Figure 14 shows the
development of physical disorder in Rotterdam in the period 2006-2011. From this figure can be
0,0
2,0
4,0
6,0
8,0
10,0
12,0
2006 2007 2008 2009 2011
0,0
5,0
10,0
15,0
20,0
2006 2007 2008 2009 2011
Drug nuisance Loitering Drunken people Harassment
38 | P a g e
derived that physical disorder is more perceived to be a neighbourhood problem than social disorder.
The percentage of citizens that thought that social disorder is a neighbourhood problem fluctuated
between 6.8 percent and 10,9 percent in this period. This percentage is higher for physical disorder
where the percentages vary between 19,7 percent and 24 percent. A reason for the higher levels of
perceived physical disorder could be the visibility of physical disorder. Physical disorder is visible until
it is fixed or removed. Physical disorder was the most perceived to be a neighbourhood problem in
2008. The development of the perceived physical disorder declined after 2008 till 2011. Although, the
level of perceived physical disorder was higher in 2011 than in 2006. This means that the perceived
physical disorder has increased in this period.
Figure 14: Development of physical disorder since 2006, Rotterdam, 2011. Source: (processed data from) Directie Veiligheid Rotterdam
When looking at the indicators of physical disorder in Rotterdam, the indicator that showed to be the
most perceived as a neighbourhood problem is dog dirt. The development of the indicators of physical
disorder in Rotterdam is presented in figure 15. Pollution on the streets is second most perceived as a
neighbourhood problem followed by litter next to the container, holes in the pavement, destruction
of phone booths, tram and bus stops, graffiti on the walls, urinating in the public and broken street
furniture. Striking is the development of litter next to the container and urinating in the public. Both
indicators were remarkably more perceived to be a neighbourhood problem after 2007. Litter next to
the container became due to this increase the third most perceived as a neighbourhood problem
between 2006 and 2011. Another point that is worth mentioning is the development of dog dirt. This
indicator had the biggest decline between 2006 and 2011. The differences with litter next to the
container and pollution on the streets has been strongly reduced.
Figure 15: Development of indicators of physical disorder since 2006, Rotterdam, 2011. Source: (processed data from) Directie Veiligheid Rotterdam
0,0
5,0
10,0
15,0
20,0
25,0
30,0
2006 2007 2008 2009 2011
0,0
5,0
10,0
15,0
20,0
25,0
30,0
35,0
40,0
2006 2007 2008 2009 2011
Graffiti Destruction LitterUrinating Holes Dog dirtPollution Broken street furniture
39 | P a g e
5.4 Magnitude of the change of disorder This section is about the percentage changes in which disorder is perceived to be a neighbourhood
problem in the districts of Rotterdam in the period 2006-2011. The percentage changes gives an
overview in which disorder is more or less perceived to be a problem according to the citizens of
Rotterdam. The percentage change of neighbourhood disorder is calculated by measuring the most
recent level of disorder (2011) against the level of neighbourhood disorder in 2006. Table 2 shows the
percentage changes of disorder, social disorder and physical disorder in the districts of Rotterdam with
respect to 2006.
Table 2 shows that disorder is more perceived to be a neighbourhood problem in almost all the districts
of Rotterdam. The perceived disorder had the strongest increase in the district Hoek van Holland. The
increase of the observation of disorder in this district is 52,7 percent. In section 5.1 was shown that
Hoek van Holland was the district with the least perceived disorder in 2006. Nonetheless, after the
increase of disorder, Hoek van Holland is still the district with the least perceived disorder in 2011.
Other districts in which disorder increased significantly are Hoogvliet (36%), Feijenoord (33,1%),
IJsselmonde (30,6%), Delfshaven (28,7%) and Overschie (22,5%). Disorder is less perceived to be a
neighbourhood problem in the districts Hillegersberg-Schiebroek, Prins Alexander and Pernis. The
decline in disorder was the strongest in Pernis with a decline of 25.5 percent followed by Prins
Alexander with 11,5 percent and Hillegersberg-Schiebroek with 2,5 percent. The level of disorder
increased 17,1 percent on average in the districts of Rotterdam in the period 2006-2011. Noteworthy
to mention is that the district Stadscentrum due to a relatively small increase of disorder dropped from
the district with the most perceived disorder to the district with the third most perceived disorder.
Disorder Physical disorder Social disorder
Charlois +16,7 +8.9 +51,5
Delfshaven +28,7 +12.6 +67,9
Feijenoord +33,1 +23.8 +61,8
Hillegersberg-Schiebroek -2,5 +0.6 +19,4
Hoek van Holland +52,7 +18,8 +196,7
Hoogvliet +36,3 +25,5 +107,5
IJsselmonde +30,4 +16,6 +60,4
Kralingen-Crooswijk +18,0 +12,8 +60,5
Noord +18,0 -1,7 +26,1
Overschie +22,5 +13,1 +178,3
Pernis -25,5 -8,3 +40,4
Prins Alexander -11,5 -1,4 +27,5
Stadscentrum +5,6 +6,5 +16,2
Table 2: Percentage change of disorder in the districts with respect to 2006, Rotterdam, 2011: Source (processed data from) Directie Veiligheid Rotterdam.
Table 2 shows that the changes in the extent that physical disorder is seen as a neighbourhood problem
are less big than that of disorder as a whole. The district in which physical disorder increased the most
is Hoogvliet. Physical disorder was 25,5 percent more perceived to be a neighbourhood problem in this
district than in 2006. The only other district that also had an increase of physical disorder of at least 20
percent is Feijenoord (23,6%). Physical disorder was less perceived to be a neighbourhood problem in
three districts. These districts are Pernis (-8,3%), Noord (-1,7%) and Prins Alexander (-1,4%).
40 | P a g e
The decline in physical disorder is not big in these districts, but this could be expected due to the fact
that disorder in general increased in the period 2006-2011. The perceived physical disorder increased
9,8 percent in the districts of Rotterdam in this period.
The percentage changes of social disorder are more extreme than the changes in disorder and physical
disorder in the districts of Rotterdam. Social disorder has been more perceived to be a neighbourhood
problem in all the districts of Rotterdam. Outliers in a negative manner are Hoek van Holland (196,7%),
Overschie (178,3%) and Hoogvliet (107,5%). The percentage of residents that thought that social
disorder is a neighbourhood problem doubled or almost tripled in these districts. The changes in these
districts are bigger than in other districts, because these three districts had the lowest levels of disorder
in 2006. Districts with a relatively smaller increase of social disorder are Prins Alexander (27,5%), Noord
(26,1%), Hillegersberg-Schiebroek (19,4%) and Stadscentrum (16,2%). The perceived social disorder
increased 70,3 percent on average in the districts of Rotterdam in the period 2006-2011.
5.5 Conclusion This section gives an answer to the third research question: What is the variation in neighbourhood
disorder and how has neighbourhood disorder developed in the districts of Rotterdam between 2006
and 2011? The disorder in neighbourhoods has been more perceived to be a neighbourhood problem
in the period 2006-2011. The development of neighbourhood disorder fluctuated and the highest level
of disorder was observed in 2008. The four districts with the most perceived disorder are Charlois,
Delfshaven, Feijenoord and Stadscentrum. Other districts with an above average perceived disorder
are Kralingen-Crooswijk and Noord.
The percentages of citizens that perceived social disorder to be a neighbourhood problem varied
between 6.8 percent and 10,9 percent in the period 2006-2011. Social disorder has been more
perceived to be a neighbourhood problem in this period. 6,8 percent of the citizens of Rotterdam
thought that social disorder was a neighbourhood problem in 2006. This increased to 10,3 percent in
2011. Loitering was the most perceived to be a neighbourhood problem of the indicators of social
disorder followed by drunken people on the streets, drug nuisance and harassment.
The percentages of citizens of Rotterdam that perceived physical disorder to be a neighbourhood
problem varied between 19,7 percent and 24 percent. The perceived physical disorder fluctuated in
the period 2006-2011. However, physical disorder has been more perceived to be a neighbourhood
problem in the end of this period. 19,7 percent of the citizens of Rotterdam that physical disorder was
a neighbourhood problem in 2006. This increased to 20,9 percent in 2011. Dog dirt is the indicator that
is most perceived to be a neighbourhood problem followed by pollution on the streets, litter next to
the container, holes in the pavement, destruction of phone booths, tram and bus stops, graffiti on the
walls, urinating in public and broken street furniture.
Disorder has increased in the period 2006-2011. Although, the percentage of citizens who thought that
disorder is a neighbourhood problem declined after 2008. Disorder is more perceived to be a
neighbourhood problem in almost all the districts. The district with the biggest increase of disorder is
Hoek van Holland. Districts in which disorder declined are Hillegersberg-Schiebroek, Prins Alexander
and Pernis. Disorder increased 17,1 percent on average in the districts of Rotterdam in the period
2006-2011. The differences in physical disorder were less extreme, but this also increased in almost all
the districts. Pernis, Noord and Prins Alexander are the only districts in which physical disorder
declined. Social disorder increased in every district. The outliers are Hoek van Holland, Hoogvliet and
Overschie.
41 | P a g e
6. Results
The fourth research question will be addressed in this chapter: What is the relationship between neighbourhood disorder and crime in the districts of Rotterdam between 2006 and 2011? The hypotheses that are formulated in the theoretical framework will be tested in this chapter. In the previous chapters the conclusion is that the crime rates declined in the period 2006-2011. Furthermore disorder has been more perceived by the residents in the same period. This chapter examines to what extent disorder and crime correlate with one another. The relationship between neighbourhood disorder and crime will be tested through correlational analyses. The relationship between disorder and crime will be tested in section 6.1. Section 6.2 contains the conclusions of this chapter and the answer to the research question.
6.1 Neighbourhood disorder and crime This section is about the relationship between neighbourhood disorder and crime. According to Skogan
(1990) neighbourhood disorder is a cause of crime. Others think that disorder and crime are correlated
by mediator variables such as fear of crime or social control (Geis & Ross, 1998; Xu et al., 2005; Gault
& Silver, 2008). There is agreement among scholars about the fact that disorder and crime are
correlated.
This analysis will examine if disorder and crime are also correlated in the districts of Rotterdam in the
period 2006-2011. In the theoretical framework the Broken Windows Theory is mentioned. This theory
suggests that minor forms of public disorder (such as broken windows) could lead to severe crime and
a downward spiral of urban decay. This makes residents fearful and causes them to retreat to their
homes. Wilson and Kelling (1982) suggest ‘that untended behavior leads to the breakdown of
community controls’. This gives room to criminals to operate freely in this neighbourhood. The Broken
Windows Theory has besides disorder and crime two other key variables. These variables are fear of
crime and social control. In the analysis of the relationship between disorder and crime are these
variables included, because they could influence the relationship between them.
In section 3.3.2 the Cronbach’s alpha of the variables is presented. It seems apparent that when all the
types of crime are pooled, the Cronbach’s alpha is too low for these variables to be one scale for crime.
As a result, crime will be examined per crime type separately. Meaning that the neighbourhood
disorder, fear of crime and social control will be examined in relation to:
Theft and burglary crimes
Destruction or damage against public law and order
Violence and sex crimes
Drugs and alcohol offenses
Hypotheses 1-6 will be tested in this section. The analysis of the hypotheses will give an answer to the
research question and a representation about the mutual correlations between the variables of the
Broken Windows Theory. Table 3 gives an overview of the results of the Spearman’s rho test between
the variables of the Broken Windows Theory. Due to the cross sectional design of this research a causal
effect cannot be concluded. We can only discuss the correlation between crime and disorder.
42 | P a g e
Table 3: Spearman’s rho test between the variables of the Broken Windows Theory (N=63). * Correlation is significant at the 0.05 level (1-tailed). ** Correlation is significant at the 0.01 level (1-tailed). *** Correlation is significant at the 0.001 level (1-tailed).
The results of the Spearman’s rho test in table 3 shows that disorder is correlated with the four types
of crime that are being used in this research. The correlation coefficients between disorder and the
types of crime are all significant and positive. This implies that neighbourhoods with a high level of
disorder are more likely to have a higher level of crime than neighbourhoods with a low level of
disorder and vice versa. Based on the results in table 3 can be derived that disorder has the strongest
correlation with drugs and alcohol offenses (r = ,702). The second strongest correlation has disorder
with violence and sex crimes (r = ,602) followed by burglary and theft crimes (r = ,426). The lowesr
correlation is between destruction and damage against public law and order crimes (r = ,385).
In this research, physical and social disorder are also separately examined to see which of the two
types has the strongest correlation with crime. Table 3 shows that both the types of disorder are
positively correlated with crime. Social disorder, however is slightly stronger correlated to crime than
physical disorder. Social disorder has the strongest relationship with drugs and alcohol offenses (r =
,690) followed by violence and sex crimes (r = ,583), theft and burglary crimes (r = ,386) and destruction
and damage against public law and order (r = ,372). Physical disorder has the same order of correlation
related to the types of crime. This means that physical disorder is also the strongest correlated with
drugs and alcohol offenses (r = ,656) followed by violence and sex crimes (r = ,605), theft and burglary
crimes (r = ,399) and destruction and damage against public law and order crimes (r = ,360).
It is noteworthy that disorder is the strongest correlated to the types of crime that are registered to a
lesser extent. Drugs and alcohol crimes were of the four types of crimes the least registered followed
by violence and sex crimes during the examination period. The Spearman’s rho test about the
relationship between disorder and crime showed that there is a significant relation between disorder
and crime. Hypothesis 1 expects that neighbourhood disorder and crime are positively correlated. The
outcomes of the Spearman’s rho test show that this is also the case in Rotterdam. Therefore can
hypothesis 1 be confirmed.
The theoretical framework indicates that neighbourhood disorder and fear of crime are correlated.
This correlation is also apparent based on data in table 3. Strong correlations are found between
Statistics Disorder Physical disorder
Social disorder
Fear of crime
Social control
Disorder Correlation Coefficient Sig. (1-tailed)
Physical disorder Correlation Coefficient Sig. (1-tailed)
Social disorder Correlation Coefficient Sig. (1-tailed)
Fear of crime Correlation Coefficient Sig. (1-tailed)
,865*** ,000
,861*** ,000
,841*** ,000
Social control
Correlation Coefficient Sig. (1-tailed)
-,684*** ,000
-,681*** ,000
-,682*** ,000
-,632*** ,000
Theft and burglary crimes Correlation Coefficient Sig. (1-tailed)
,426*** ,000
,399*** ,001
,386*** ,001
,503*** ,000
,023 ,430
Destruction or material damage against public law and order
Correlation Coefficient Sig. (1-tailed)
,385*** ,001
,360*** ,000
,372*** ,000
,425*** ,000
,067 ,301
Violence and sex crimes Correlation Coefficient Sig. (1-tailed)
,602*** ,000
,605*** ,000
,583*** ,000
,632*** ,000
-,254* ,022
Drugs and alcohol offenses Correlation Coefficient Sig. (1-tailed)
,702*** ,000
,656*** ,000
,690*** ,000
,688*** ,000
-,357*** ,000
43 | P a g e
neighbourhood disorder and fear of crime. The relationship between disorder and fear of crime has a
correlation coefficient of ,865. Meaning that neighbourhoods with a high level of disorder are more
likely to have a higher level of fear of crime than neighbourhoods with a lower level of disorder and
vice versa. Physical disorder and social disorder are also examined in relation to fear of crime. Both the
types of disorder have also a strong significant correlation with fear of crime. The relationship between
physical disorder and fear of crime has a correlation coefficient of ,861 and the relationship between
social disorder and fear of crime has a correlation coefficient of ,841. Physical disorder has a slightly
stronger correlation with fear of crime than social disorder. The results of the Spearman’s rho test
show that disorder is correlated with fear of crime. Hypothesis 2, can therefore be confirmed.
A lot is written about the relationship between social control and disorder in this thesis. The literature
shows that social control and disorder are correlated to one another. When disorder increased in
neighbourhoods social control decreases in that neighbourhood (Markowitz et al., 2001. The
Spearman’s rho test could not determine whether social control is a cause of disorder, but it could test
the correlation between disorder and social control. Hypothesis 3 expects that the relationship
between social control and disorder is negative. Table 3 confirms this assumption. There is indeed a
negative correlation between social control and disorder. The correlation coefficient of this
relationship is -,684 and is significant at the 0.1% level. The results of the Spearman’s rho test also
show that physical disorder and social disorder have significant correlations with social control. The
difference between the two types of disorder is minimal, because the relationship of social control and
physical disorder has a correlation coefficient of -,681 whilst the relationship between social disorder
and social control has a correlation coefficient of -,682. The results of the Spearman’s rho test show a
significant negative correlation between disorder and social control, which means that hypothesis 3
can be confirmed.
The literature states that fear of crime makes people retreat to their homes (Wilson & Kelling, 1982).
As a result, the interaction between neighbors will diminish. This lowers the social control in a
neighbourhood. To see if this assumption is correct, is the relationship between fear of crime and social
control is examined in this research. The assumption is that when people are more fearful of crime,
the neighbourhood shows a lower level of social control. Hypothesis 4 is related to this relationship.
The results of the Spearman’s rho test indicate that there is a significant negative correlation between
fear of crime and social control. The correlation coefficient of this relationship is -,632. This means that
hypothesis 4 can be confirmed.
A result of the increase of fear of crime and the decrease of social control in a neighbourhood is that
crime will increase, because criminals will get more room to operate in this neighbourhood (Wilson &
Kelling, 1982). People do not correct misbehavior and do not monitor the public space. This could lead
to that criminals getting away with their actions unpunished. Hypothesis 5 concerns the relationship
between fear of crime and crime. Table 3 indicates that there is a significant relationship between fear
of crime and crime. The correlation coefficients of the relationship between fear of crime and the
different types of crime are all significant at the 0,1% level. The strongest correlation was found
between drugs and alcohol offenses and fear of crime (,688) followed by violence and sex crimes (,632),
theft and burglary crimes (,503) and destruction or material damage against public law and order
(,425). This means that the results of the Spearman’s rho test confirm hypothesis 5.
The last hypothesis concerns the relationship between social control and crime. This relationship is
expected to be a negative relationship. The lack of social control is often seen as a cause for crime (van
Stokkum & Toenders, 2010; Xu et al., 2005; Markowitz et al, 2001). The Spearman’s rho test partially
44 | P a g e
supports this hypothesis, because social control had a significant correlation with drugs and alcohol
offenses (-,357) and violence and sex crimes (-,254). These correlations are in the expected direction.
The Spearman’s rho test was unable to find a significant correlation between social control and theft
and burglary crimes, and destruction or material damage against public law and order.
Almost all of the relationships between the variables that are mentioned in the Broken Windows
Theory can be confirmed with the Spearman’s rho test. The only relationship that could not fully be
supported by the data is the relationship between social control and crime. When the results of the
Spearman’s rho test are added to figure 3, the result is in figure 16.
-,684***
,865*** -,632***
Theft and burglaryDestruction
Violence and sexDrugs and alcohol
,503***,425***,632***,688***
Fear of crime Social controlNeigborhood
disorder
,426***,385***,602***,702***
,023,067
-,254* -,357***
Figure 16: Relations between the variables of the Broken Windows Theory. * Correlation is significant at the 0.05 level (1-tailed).
*** Correlation is significant at the 0.001 level (1-tailed).
The relations between the variables within the Broken Windows Theory with their mutual correlations
are shown in figure 16. A difference with figure 3 is that crime is divided in the four types of crime that
are used in this research. Some lines within figure 16 show four correlations. These correlations
represent the correlation between the variables with the type of crime in the order of how the types
of crimes are mentioned. For example, the correlation coefficient of ,503 between fear of crime and
the types of crime is the correlation with theft and burglary, because it is the first mentioned
correlation coefficient.
Figure 16 shows that neighbourhood disorder and fear of crime have strong correlations with crime.
The strongest correlation between the variables of the Broken Windows Theory is measured between
disorder and fear of crime. The strong correlation between fear of crime and disorder raises a question,
namely is the significant relationship between neighbourhood disorder and crime a self-contained
relationship or is it mediated or explained by fear of crime? In previous research is found that the
relationship between neighbourhood disorder and crime is mediated by fear of crime (Markowitz,
Bellair, Liska, & Liu, 2001). A partial correlation test is computed to examine the strength of the
correlation between disorder and the types of crime. The results of the partial correlation test are
shown in figure 17, which is presented on the next page. The lines with the correlation coefficient
represent the relationship between the two variables corrected for the other variable. For example,
the correlation coefficient of ,844 between neighbourhood disorder and fear of crime is corrected for
theft and burglary crimes.
45 | P a g e
The correlations between disorder, fear of crime, and the types of crime change after the partial
correlations test. The relationships between fear of crime, disorder and the types of crime were all
significant before the partial correlations test. Two relationships remain significant with crime after
the partial correlations test. This is the relationship between neighbourhood disorder and violence and
sex crimes and the relationship between neighbourhood disorder and drugs and alcohol offenses. The
relationship between neighbourhood disorder and drugs and alcohol offenses has the strongest
correlation coefficient of ,342 with and a significance level of ,003. The relationship between
neighbourhood disorder and violence and sex crimes has a slightly weaker correlation of ,220 with a
significance level of ,043. This means that the relationships between neighbourhood disorder and theft
and burglary crimes and neighbourhood disorder and destruction or material damage against public
law and order crimes became insignificant after controlling for fear of crime. This could mean that
those relationships are explained by fear of crime, but this seems not to be the case. The relationships
of fear of crime with the types of crime become insignificant after controlling for neighbourhood
disorder. This implies that neighbourhood disorder has a better correlation with the types of crime
than fear of crime. Suggesting that neighbourhood disorder is a better predictor of crime than fear of
crime, especially in the cases of violence and sex crimes and drugs and alcohol offenses. This could be
an outcome of the partial correlation’s test that is surprising. However, is fear of crime such a good
predictor for crime? Where the crime rates are declining in the Netherlands, the development of fear
of crime among citizens is consistent or even increasing in the last years (Centraal Bureau voor de
Statistiek, 2013b, 2014).
Figure 17: Partial correlation test between neighbourhood disorder, fear of crime and the types of crime.
The results from the analysis, show that the variables of the Broken Windows Theory correlate with
one another, based on the data from the districts of Rotterdam. The main implication of these results
is that neighbourhoods of Rotterdam with higher levels of disorder, more often have higher levels of
fear of crime, crime and a lower level of social control than neighborhoods with lower levels of
disorder. This means that the Broken Windows Theory could explain the relationship between
neighborhood disorder and crime in Rotterdam. Implying that untended disorder could lead to crime
and a downward spiral of urban decay.
46 | P a g e
6.2 Conclusion This chapter addressed the question: What is the relationship between neighbourhood disorder and
crime in the districts of Rotterdam between 2006 and 2011? The analysis shows that neighbourhood
disorder correlates with crime based on the data from the districts of Rotterdam. Neighbourhood
disorder correlated with all the types of crime that are used in this research. The strongest correlation
was found with drugs and alcohol offenses followed by violence and sex crimes, theft and burglary
crimes, and destruction or material damage against public law and order.
The differences between the types of disorder and crime were minimal. Social disorder is in general
slightly stronger correlated than physical disorder with the types of crime. Nevertheless, physical
disorder was slightly stronger correlated to theft and burglary crimes, and violence and sex crimes.
Whereas social disorder was slightly stronger correlated to destruction or damage against public law
and order crimes, and drugs and alcohol offenses. This means that neighborhoods with a high level of
physical disorder are more likely to have higher levels of violence and sex crimes, and theft and
burglary crimes than neighborhoods with a high level of social disorder. The reverse applies to the
other types of crime.
The relationship between neighbourhood disorder and crime is often related to the Broken Windows
Theory. The mutual correlations between the variables of this theory has been examined in this
chapter. Between the variables were strong significant correlations found. The only exception is
between social control and the types of crime. Social control does have a significant correlation with
violence and sex crimes and drugs and alcohol offenses. This implies that the Broken Windows Theory
could explain the relationship between neighbourhood disorder and crime, also in Rotterdam.
Meaning that untended disorder could lead to crime and a downward spiral of urban decay.
47 | P a g e
7. Conclusion, discussion, contribution, and limitations of this
research
This chapter addresses the conclusion, discussion, contributions to the literature, and limitations of
this research. The central research question will be answered in section 7.1 and is based on the results
that were presented in the previous chapters. The results of this research in relation to the literature
and the contributions that are made will be discussed in section 7.2. Section 7.3 gives
recommendations. The last section is about the limitations of this research and it also contains the
validity and reliability of the variables.
7.1 Conclusion This section answers the central research question which is formulated as follows: “To what extent
does neighbourhood disorder change in the districts of Rotterdam in the period of 2006-2011 and does
neighbourhood disorder explain crime changes?” The level of disorder that is perceived by citizens has
fluctuated in the period 2006-2011. Disorder was less perceived to be a neighbourhood problem in
2007 than in 2006. It did increase to its highest point in the year 2008. After 2008, the level declined
every year. It was still higher than in 2006 though. The perceived disorder totally increased by 17,1
percent on average in the districts of Rotterdam. The perceived disorder increased in almost all the
districts of Rotterdam. The district in which the perceived disorder did increase the most was Hoek van
Holland. Here it almost tripled. On the other hand, the strongest decline of perceived disorder was in
the district Pernis. The decrease of perceived disorder was also present in Hillegersberg-Schiebroek
and Prins Alexander.
The results presented in this thesis show that neighbourhood disorder and crime are positively
correlated. Drugs and alcohol offenses showed to have a strong correlation with disorder. Violence
and sex crimes, and theft and burglary crimes showed to have a moderate correlation with disorder.
Destruction or damage against the public law and order showed to have a weak correlation with
disorder. The differences between the types of disorder and crime were minimal. Social disorder is in
general slightly stronger correlated than physical disorder with the types of crime. Nevertheless,
physical disorder was slightly stronger correlated to theft and burglary crimes, and violence and sex
crimes. Whereas social disorder was slightly stronger correlated to destruction or damage against
public law and order crimes, and drugs and alcohol offenses. Neighbourhood disorder showed of the
variables in this research to be the best predictor of crime.
The relationship between neighbourhood disorder and crime is often related to the Broken Windows
Theory. The mutual correlations between the variables of this theory has been strong significant
correlations were found. The only exception is between social control and the types of crime. Social
control does have a significant correlation with violence and sex crimes, and drugs and alcohol
offenses. The strong correlations between the variables show that neighbourhoods of Rotterdam with
higher levels of disorder, have more often higher levels of fear of crime, crime and a lower level of
social control than neighborhoods with lower levels of disorder. This implies that the Broken Windows
Theory could explain the relationship between neighbourhood disorder and crime, also in Rotterdam.
Meaning that untended disorder could lead to crime and a downward spiral of urban decay.
7.2 Contributions to the literature This section describes the contributions of this research to the existing literature. The results will be
compared with the literature described in the theoretical framework. Much has been written about
48 | P a g e
the relationship between disorder and crime in the theoretical framework. The similarities and
differences with the literature and this research will be discussed in this section.
The relationship between neighborhood disorder and crime is an unclear relationship. This research
shows a linear correlation between disorder and crime. Between the types of crime and disorder are
weak to strong correlations found. The weak correlation is found between destruction or material
damage against the public law and order and disorder. This type of crime is not often examined in
relation to disorder. This research examines to what extend neighbourhood disorder explains crime in
the districts in Rotterdam. Nonetheless showed the weak correlation that a linear relationship exists
between disorder and destruction or material damage against the public law and order crimes.
Moderate correlations with disorder have been found with theft and burglary crimes, and violence and
sex crimes. These two types are the most examined with regard to disorder in the literature (Sampson
& Raudenbush, 1999; Skogan, 1990; Xu et al., 2005). In some studies theft and burglary correlated
stronger than violence and sex crimes and vice versa. This research found a stronger correlation
between disorder and violence and sex crimes. This is consistent with earlier conducted studies
(Boggess & Maskaly, 2014; Sampson & Raudenbush, 1999).
The strongest correlation was found between drugs and alcohol offenses and disorder. This is also a
type of crime that is not much examined in relation to neighbourhood disorder. This could be due to
the fact that drugs and alcohol offenses are less reported than the other types of crimes used in this
research. That disorder has the strongest correlation with drugs and alcohol offenses shows that in
neighbourhoods were disorder can be found, it is more likely to find drugs and alcohol offenses than
the other types of crime.
The relationship between disorder and fear of crime is examined in the literature and this is also
discussed in the theoretical framework. The conclusion of most studies regarding this relationship is
that perceived disorder makes residents fearful (Brunton‐Smith & Sturgis, 2011; Kohm, 2009; Skogan,
1990). The strong correlation found between fear of crime and disorder confirms that neighbourhoods
with a higher level of disorder also have higher levels of fear of crime than neighbourhoods with a
lower level of disorder. This outcome implies that perceived neighborhood disorder makes residents
fearful. In the theoretical framework is also mentioned that some authors think differently about the
effect of social disorder and physical disorder on fear of crime. Kohm (2009) found that social disorder
could be linked to spatial patterns and that physical disorder does not tend to provoke fear for citizens.
In contrary, Brunton‐Smith and Sturgis (2011) demonstrated that visible signs of neighbourhood
disorder are a highly predictive of expressed fear of crime. The results found in this research show that
both the types are strongly correlated to fear of crime. However, physical disorder was slightly stronger
correlated.
The relationship between neighbourhood disorder and social control is also discussed in the
theoretical framework. It came forward that disorder leads to the decrease of social control.
Deteriorated and decayed neighbourhoods lead to that residents do not trust their neighbors, do not
have social ties and therefore do not correct misbehavior (Oh, 2003; Ross & Jang, 2000; Ross et al.,
2001). This research found a strong negative correlation between social control and neighborhood
disorder. That is consistent with the findings of the studies discussed in the theoretical framework,
because the results of this research indicate that neighbourhoods with a higher level of disorder have
a lower level of social control than neighbourhoods with a lower level of disorder and vice versa. This
indicates that neighborhood disorder affects the level of social control in a neighborhood.
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The Broken Windows Theory is often discussed in relation to the relationship between crime and
disorder. This research showed that all the variables of the Broken Windows Theory have a linear
relationship in the expected direction. Although, social control was only correlated with violence and
sex crimes and drugs and alcohol offenses. This means that the Broken Windows Theory could be
plausible in the cases of violence and sex crimes, and drugs and alcohol offenses. The strong
correlations found between disorder, fear of crime and social control indicates that these variables are
closely connected and that they could influence each other. Meaning that the correlations found in
this research make it plausible that the broken windows theory could explain the relationship between
neighborhood disorder and crime.
The results of the analysis confirmed nearly all the hypotheses completely, except hypothesis 6 which
is partially confirmed. This means that the variables that are used in this research are connected to
each other. Implying that it could be useful to take these variables into account when making policies
about neighbourhood safety. Neighbourhood disorder could lead to the deterioration of a
neighbourhood. Keizer et al. (2008) showed that that inappropriate behavior like broken windows or
graffiti lead to other inappropriate behavior, which in turn results in the inhibition of norms. It is also
shown that neighbourhood disorder could make people feel themselves unsafe in their own
neighbourhood (Brunton‐Smith & Sturgis, 2011; Kohm, 2009; Skogan, 1990). In earlier studies is also
shown that residential stability has an relationship with disorder in neighbourhoods (Wilcox et al.,
2004). In addition, it was found that local ties and attachment are linked to residential stability (Taylor,
1997). As a result could neighbourhood disorder make people retreat to their homes or move out of
the neighbourhood. Disorder, fear of crime and social control are proven to be related in earlier studies
(McGarrell et al., 1997; Ross & Jang, 2000). Decayed and deteriorated neighbourhoods are also less
attractive for more prosperous citizens. It is shown that neighbourhoods with more concentrated
disadvantage have more neighbourhood disorder (Sampson & Raudenbush, 1999). The correlations
found in this research confirm the mutual relationships of the Broken Windows Theory and this makes
it plausible, also in Rotterdam, that neighbourhood disorder leads to crime and a downward spiral of
urban decay (Wilson & Kelling, 1982).
7.3 Recommendations This section describes recommendations based on the findings of this research. The findings of this
research showed that the Broken Windows Theory variables are closely connected to each other.
Implying that when one of the variables is affected this could also affect another variable. The first
recommendation is that disorder in neighbourhoods should be tackled. This means that troublesome
loiterers, drunken people on the streets, people who harass other people, drugs related nuisance,
vandalism and pollution in the neighbourhood should be tackled. This will not only improve the
livability of a neighbourhood, but should also have positive effects on fear of crime, the degree of social
control in a neighbourhood and crime. This recommendation is consistent with the current safety
approach of Rotterdam for the period 2014-2018. Important topics of this approach are:
The general level of safety should be alright;
Citizens and entrepreneurs should feel themselves more safe in their own neighbourhood;
Children must be able to be safe when growing in the city. Youth nuisance and juvenile
delinquency should be restricted.
Fear of crime is a concept that is on the agenda of municipalities for a considerable time now. The last
years, fear of crime didn’t decline in the Netherlands (Centraal Bureau voor de Statistiek, 2014). It even
50 | P a g e
increased. Especially the number of citizens that feel themselves sometimes unsafe in their own
neighbourhood increased with more than ten percent in the period 2008-2013. This research
demonstrated that neighbourhood disorder and fear of crime are strongly correlated in Rotterdam. To
reduce fear of crime, among citizens of Rotterdam, it could be useful to focus more on disorder in
neighbourhoods than on crime. Being a victim of crime and neighbourhood disorder affect fear of
crime (Gainey, Alper, & Chappell, 2011). However, neighbourhood disorder is more visible and more
present in a neighbourhood (Gau & Pratt, 2008). The chance of being a victim of violence and sex
crimes is 2 percent in Rotterdam and the chance of being a victim of theft and burglary is 13 percent
in Rotterdam (Centraal Bureau voor de Statistiek, 2014). This indicates that the chances of becoming
a victim of crime are not that big. Other studies showed that neighbourhood disorder leads to fear of
crime (Brunton‐Smith & Sturgis, 2011; Kohm, 2009). Tackling disorder in neighbourhoods should
therefore have a positive effect on the fear of crime of citizens.
When the fear of crime decreases in a neighbourhood this leads to an increase of social control in that
neighbourhood (McGarrell et al., 1997). Not only because people withdraw themselves less in their
own homes, but also because neighbourhood disorder is connected with social control. This research
demonstrates that neighbourhood disorder, fear of crime and social control are closely connected.
Neighbourhood disorder also facilitates mistrust and people who live in neighbourhoods with higher
levels of disorder tend to have fewer ties with their neighbors and participate less in neighbourhood
organizations (Ross & Jang, 2000; Ross et al., 2001). This means that tackling disorder in
neighbourhoods should have a positive effect on the social control in a neighbourhood. This should
improve trust and ties among neighbors, and the participation of citizens in neighbourhood
organizations. The participation and involvement of citizens with their neighbourhood is important in
the safety approach of Rotterdam for the period 2014-2018 (Gemeente Rotterdam, 2013). Citizens
have the opportunity to indicate which problems should be tackled in their neighbourhood. More
involvement and participation of citizens with their neighbourhood should only improve the
effectiveness of the safety approach of Rotterdam.
The findings of this research demonstrated that crime is correlated with the other mentioned
variables. Other studies showed that neighbourhood disorder, fear of crime and a decrease of social
control lead to crime (Boggess & Maskaly, 2014; Cheong, 2012; Sampson & Raudenbush, 1999; Xu et
al., 2005). The decrease of neighbourhood disorder and fear of crime and an increase of social control
should lead to a decline in crime rates.
Some attention should be paid to Broken Windows Policing or Order Maintenance Policing. This type
of policing is a police practice involving the management of minor offenses and neighbourhood
disorders in order to address community problems. After recommending that disorder should be
tackled, it seems obvious to recommend Order Maintenance Policing. But the effects of Order
Maintenance Policing on crime are not proven (Harcourt, 2001; Weisburd et al., 2010). Also multiple
organizations are involved in the safety approach of Rotterdam, meaning that the execution of this
safety approach not only lies by the police (Gemeente Rotterdam, 2013).
It is also useful to speak about improvements that can be made with future research. The units of
analysis of this research are neighbourhoods and on a higher aggregative level districts of Rotterdam.
The number of neighbourhoods of Rotterdam is too small to perform a regression analysis in which
disorder could be controlled for more than one variable. Therefore if more neighbourhoods, for
example neighbourhoods of other Dutch municipalities, would be added to the units of analysis, a
regression analysis with more variables would be possible. This would result in a better indication to
51 | P a g e
which extent disorder, fear of crime and social control are strong predictors of crime in the
Netherlands. It could also be insightful to include a time order to see if neighbourhood disorder in a
certain year is correlated with crime in the next year. This could give insights if less/more
neighbourhood disorder in a year leads to less/more crime in the next year.
7.4 Limitations This section describes the limitations of this research, being mostly related to the chosen design of this
study, and the reliability and validity of the variables. Validity refers to the quality of the measures;
meaning that what is measured was indeed supposed to be measured. Reliability refers to the
consistency of the measurements and the repeatability of these measurements (Brinkman, 2006). The
variables that are used in this research are high on validity, but lower on reliability.
The validity of the measures used for the variables neighbourhood disorder, fear of crime, social
control and the reported crimes to the police are high on validity. The data about disorder, social
control and fear of crime are acquired through surveys held among citizens of Rotterdam. In the
surveys questions were asked based on the interests of the different organizations involved and
therefore they score high on validity. For instance, the safety monitor asked questions about safety
feelings, victimization and livability, whereas the social index asked questions about social ties,
participation of citizens in the neighbourhood and trust among neighbors. The reported crimes to the
police are all objective changes of police performances and achievements; this ensures they are also
high on validity. However, there is a dark number in the crime rates, meaning that the reported crimes
do not reflect the total crime. The dark number are crimes that are not observed and registered by the
police. For instance, a drug dealer who never get caught selling drugs. Nonetheless, the dark number
is not preventable. Reported crimes give an implication of the crime rates and the development of the
crime rates.
The measures used for the variables in this thesis are lower on reliability. The surveys that acquired
data for the variables neighbourhood disorder, fear of crime and social control were held among
citizens of Rotterdam. The reliability risk comprises the fact that that the willingness of the citizens
from different districts or neighbourhoods to participate is not equal. Also the characteristics of the
participants could differ; it could e.g. be the case that people who feel themselves safe in their
neighbourhood are more willing to participate with the survey than people who don’t feel safe.
However, the number of participants is taken into account during the distribution of the surveys to
ensure that the sample of the participating citizens was representative of the citizens of Rotterdam.
Meaning that the participants of the surveys are e.g. not only rich white citizens, but also people from
ethnic minorities, elderly people and so forth. Reported crimes are also lower on reliability than on
validity, because there are certain aspects that are subjected to reported crimes, such as the
willingness of the citizens to report crimes to the police, the willingness of police officers to document
reported crimes and the efforts that police officers are willing to put in the documentation of crimes.
A limitation of this research is the use of a cross-sectional design, which hampered causal inferences.
This is due to the lack of a time order in which the variables precede one another. Therefore it was
only possible to measure the correlation between the variables. In addition, it was not possible to
examine if disorder is a cause of crime, because it could not be established which of the two came first.
The lack of a time order made it also impossible to test if the Broken Window Theory could be
confirmed. Another aspect that made it impossible to draw causal inferences is the omitted variable
bias. This research only examined if disorder, fear of crime, social control and crime are associated to
52 | P a g e
each other and that means that other variables that could explain crime are not taken into
consideration, including socio-demographic characteristics of the districts of Rotterdam.
Another limitation could be that citizens are not always able to distinguish disorder from crime (Gau &
Pratt, 2008), these could blend together in the eyes of the citizens. This is not unthinkable, because
crime and disorder are seen as two opposite ends of the same continuum (Sampson & Raudenbush,
1999). The variables disorder and crime are measured as two different concepts. Crime is measured as
the actual crime and disorder as feelings towards indicators of disorder. However, in the measurement
of disorder it could be the case that the opinion of citizens is affected by crime. For instance, citizens
could see drugs trafficking and drugs nuisance as the same phenomenon. This could have led to
stronger correlations between disorder and drugs and alcohol offenses. Although, residents of
disordered areas may develop a sharper grasp on the distinction between routine nuisance and true
danger (Gau & Pratt, 2010). Overlap between crime and disorder is probable, resulting in stronger
correlations. If this is the case the outcomes of this research regarding the relationship between
disorder and crime should be nuanced, but the differences with the correlations between the crime
types and disorder, social disorder and physical disorder are minor. This implies that, even when some
indicators that could affect the strength of the correlation are left out, the correlations don’t differ
much. The number of indicators also reduces the impact of an indicator on the correlation with a
certain type of crime.
These limitations don’t imply that this research does not have a contribution in a reliable and valid
manner to this field of research. This research confirmed many relationships with regard to
neighbourhood disorder and the Broken Windows Theory. In addition, this research is one of the few
correlational studies that examined the relationship between neighbourhood disorder and crime in
Rotterdam and possibly also in the Netherlands.
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Appendix A Description of the districts of Rotterdam
This appendix describes demographic and socioeconomic factors of the districts. The information that
is used in this paragraph is derived from the Neighbourhood Monitor Rotterdam.
Figure 18: Overview of where the districts are situated (Gemeente Rotterdam, 2014)
Charlois
Charlois is situated on the southern shore of the Maas. This district consists of the following
neighbourhoods: Carnisse, Heijplaat, Oud-Charlois, Pendrecht, Tarwewijk, Wielewaal, Zuidwijk and
Zuidplein. This district counted 64.050 residents on a surface of ten square kilometers in 2011. Charlois
consisted on average out of 44 % natives, 10% western immigrants and 45% non-western immigrants
between 2006 and 2011. The biggest group of immigrants came from Suriname. The unemployment
rate of the residents averaged eight percent between 2006 and 2011. The ratio between rental homes
and owner-occupied houses is 71 versus 29%, respectively. The average number of unoccupied houses
was eleven percent in the examined period.
Delfshaven
Delfshaven is situated on the right bank of the river Nieuwe Maas. This district consists of the following
neighbourhoods: Delfshaven/Schiemond, Bospolder/Tussendijken, Spangen, Oud-Mathenesse,
Nieuw-Mathenesse, Nieuwe Westen and Middelland. This district counted 73.447 residents on a
surface of 5,8 square kilometers in 2011. Delfshaven consisted on average of 29% natives, 11% western
immigrants and 60% non-western immigrants. The biggest group of immigrants originates from
Turkey. The unemployment rate of the residents averaged eight percent between 2006 and 2011. The
ratio between rental homes and owner-occupied houses is 77 versus 23 %, respectively. The average
number of unoccupied houses was ten percent in the examined period.
Feijenoord
Feijenoord is situated on the southern shore of the Nieuwe Maas. This district consists of the following
neighbourhoods: Afrikaanderwijk, Bloemhof, Feijenoord, Hillesluis, Katendrecht, Kop van Zuid,
Noordereiland and Vreewijk. This district counted 71.532 residents on a surface of 6,44 square
kilometers (of which 1,45 square kilometers water) in 2011. The average number of natives was 35%,
8% were western immigrants and 57% of the residents were non-western immigrants. The biggest
group of immigrants originates from Turkey. The unemployment rate of the residents averaged nine
57 | P a g e
percent between 2006 and 2011. The ratio between rental homes and owner-occupied houses is 77
versus 23% percent, respectively. The average number of houses that were unoccupied was ten
percent in the examined period.
Hillegersberg-Schiebroek
Hillegersberg-Schiebroek is situated in the north of Rotterdam. This district consists of the following
neighbourhoods: Hillegersberg Noord, Hillegersberg Zuid, Molenlaankwartier, Terbregge, and
Schiebroek. This district counted 42.554 residents on a surface of 12,56 square kilometers in 2011. The
average number of natives was 74%, 11% western immigrants and 15% percent of the residents non-
western immigrants. The biggest group of immigrants originates from Suriname. The unemployment
rate of the residents averaged three percent between 2006 and 2011. The ratio between rental homes
and owner-occupied houses is 1. The average number of unoccupied houses was seven percent in the
examined period.
Hoek van Holland
Hoek van Holland is situated on the northern shore of the Nieuwe Waterweg where it reaches the
North Sea. Hoek van Holland is therefore much more westerly situated than the majority of the
districts of Rotterdam. This district consists of the following neighbourhoods: Strand en Duinen,
Rijnpoort and Dorp. This district counted 9.664 residents on a surface of 14,1 square kilometers in
2011. The average number of natives was 89 %, 7% were western immigrants and 4% of the residents
was non-western immigrant. The unemployment rate of the residents averaged two percent between
2006 and 2011. The ratio between rental homes and owner-occupied houses is 57 versus 43% percent,
respectively. The average number of unoccupied houses is four percent in the examined period.
Hoogvliet
Hoogvliet is situated at the south side of Rotterdam, along the shores of the Oude Maas, in the heart
of the industrial port. Hoogvliet is divided into Hoogvliet Noord and Hoogvliet Zuid. This district
counted 34051 residents on a surface of 10,73 square kilometers in 2011. The average number of
natives was 67 %, 9% were western immigrants and 24% of the residents was non-western immigrant.
The biggest group of immigrants originates from Suriname. The unemployment rate of the residents
averaged four percent between 2006 and 2011. The ratio between rental homes and owner-occupied
houses is 64 versus 36% percent, respectively. The average number of unoccupied houses is eight
percent in the examined period.
IJsselmonde
IJsselmonde is situated in the southeast corner of Rotterdam. This district consists of the following
neighbourhoods: Beverwaard, Groot IJsselmonde, Lombardijen and Oud IJsselmonde. This district
counted 58430 residents on a surface of 13,12 square kilometers in 2011. The average number of
natives was 59 %, 9% were western immigrants and 32% of the residents was non-western immigrant.
The biggest group of immigrants originates from Suriname. The unemployment rate of the residents
averaged six percent between 2006 and 2011. The ratio between rental homes and owner-occupied
houses is 67 versus 33% percent, respectively. The average number of unoccupied houses is five
percent in the examined period.
Kralingen-Crooswijk
Kralingen-Crooswijk is situated in the east of Rotterdam. This district consists of the following
neighbourhoods: de Esch, Kralingen West, Kralingen Oost, Kralingse Bos, Nieuw Crooswijk, Oud
Crooswijk, Rubroek and Struisenberg. This district counted 49880 residents on a surface of 12,9 square
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kilometers in 2011. The average number of natives was 52 %, 13% were western immigrants and 35%
of the residents was non-western immigrant. The biggest group of immigrants originates from
Morocco. The unemployment rate of the residents averaged six percent between 2006 and 2011. The
ratio between rental homes and owner-occupied houses is 75 versus 25% percent, respectively. The
average number of unoccupied houses is nine percent in the examined period.
Noord
Noord is situated in the north of Rotterdam. This district consists of the following neighbourhoods:
Agniesebuurt, Bergpolder, Blijdorp, Blijdorpsepolder, Liskwartier, Oude Noorden and Provenierswijk.
This district counted 50495 residents on a surface of 5,37 square kilometers in 2011. The average
number of natives was 50%, 12% were western immigrants and 38% of the residents was non-western
immigrant. The biggest group of immigrants originates from Morocco. The unemployment rate of the
residents averaged seven percent between 2006 and 2011. The ratio between rental homes and
owner-occupied houses is 69 versus 31% percent, respectively. The average number of unoccupied
houses is nine percent in the examined period.
Overschie
Overschie is situated in the north of Rotterdam. This district consists of the following neighbourhoods
Kleinpolder, Landzicht, Noord Kethel, Overschie, Schieveen and Zestienhoven. This district counted
50495 residents on a surface of 15,8 square kilometers in 2011. The average number of natives was
67%, 9% were western immigrants and 24% of the residents was non-western immigrant. The biggest
group of immigrants originates from Suriname. The unemployment rate of the residents averaged five
percent between 2006 and 2011. The ratio between rental homes and owner-occupied houses is 59
versus 41% percent, respectively. The average number of houses that were unoccupied is eight percent
in the examined period.
Pernis
Pernis is situated between the ports of Rotterdam. This district counted 4789 residents on a surface of
1,6 square kilometers in 2011. The average number of natives was 85%, 7% were western immigrants
and 8% of the residents was non-western immigrant. The unemployment rate of the residents
averaged two percent between 2006 and 2011. The ratio between rental homes and owner-occupied
houses is 35 versus 65% percent, respectively. The average number of unoccupied houses is seven
percent in the examined period.
Prins Alexander
Prins Alexander is the most eastern part of Rotterdam. This district consists of the following
neighbourhoods: Het Lage Land, Kralingseveer, Nesselande, Ommoord, Oosterflank, Prinsenland, ‘s
Gravenland and Zevenkamp. This district counted 92640 residents on a surface of 20,24 square
kilometers in 2011. The average number of natives was 71%, 10% were western immigrants and 19%
of the residents was non-western immigrant. The biggest group of immigrants originates from
Suriname. The unemployment rate of the residents averaged four percent between 2006 and 2011.
The ratio between rental homes and owner-occupied houses is 62 versus 38% percent, respectively.
The average number of unoccupied houses is five percent in the examined period.
Stadscentrum
Stadscentrum is situated in the middle of Rotterdam. This district consists of the following
neighbourhoods: Cool, CS Kwartier, Dijkzigt, Nieuwe Werk, Oude Westen, Stadsdriehoek. This district
counted 30243 residents on a surface of 4,81 square kilometers in 2011. The average number of
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natives was 48%, 16% were western immigrants and 36% of the residents was non-western immigrant.
The largest group of immigrants originate from Suriname. The unemployment rate of the residents
averaged four percent between 2006 and 2011. The ratio between rental homes and owner-occupied
houses is 62 versus 38% percent, respectively. The average number of unoccupied houses is eleven
percent in the examined period.
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Appendix B Overview of the variables
This appendix provides an overview of the variables. Table 4 gives an overview of the variables that
were used in this research: the domain they belong to, how they were measured and used in the
analyses and to which period the data refer.
Predictor domain Underlying indicators Variable description Period
Theft and burglary crimes Mugging Total registered cases of mugging 2006-2011 Pickpocketing Total registered cases of pickpocketing 2006-2011
Shoplifting Total registered cases of shoplifting 2006-2011 Theft/burglary property Total registered cases of theft/burglary property 2006-2011
Theft/burglary box/garage/barn/shed
Total registered cases of theft/burglary box/garage/barn/shed
2006-2011
Theft out of/from motor vehicles
Total registered cases of theft out of/from motor vehicles 2006-2011
Theft on/off/from other vehicles
Total registered cases of theft on/off/from other vehicles 2006-2011
Theft of (light) mopeds and bicycles
Total registered cases of theft of (light) mopeds and bicycles
2006-2011
Theft of motor vehicles Total registered cases of theft of motor vehicles 2006-2011 Raid Total registered cases of raid 2006-2011
Other property crimes Total registered cases of other property crimes 2006-2011 Destruction and damage against public law and order
Destruction or material damage
Total registered cases of destruction or material damage
2006-2011
Discrimination
Total registered cases of discrimination 2006-2011
Publicly violence (person)
Total registered cases of publicly violence 2006-2011
Undermining of the public order
Total registered cases of undermining of the public order 2006-2011
Violence- and sex crimes Maltreatment Total registered cases of maltreatment 2006-2011
Threat Total registered cases of threats 2006-2011 Sexual offense Total registered cases of sexual offenses 2006-2011 Murder, manslaughter Total registered cases of murder, manslaughter 2006-2011
Drugs and alcohol offenses
Drug/alcohol nuisance Total registered cases of drug/alcohol nuisance 2006-2011
Drug trafficking Total registered cases of drug trafficking 2006-2011 Social disorder Drug nuisance % of citizens who thought that drug nuisance is a
neighbourhood problem 2006-2011
Loitering % of citizens who thought that loitering is a neighbourhood problem
2006-2011
Drunken people % of citizens who thought that drunken people in the streets is a neighbourhood problem
2006-2011
Harassment % of citizens who thought that being harassed on the streets is a neighbourhood problem
2006-2011
Physical disorder Graffiti on walls and buildings
% of citizens who thought that graffiti on walls and buildings is a neighbourhood problem
2006-2011
Destruction of phone booths, bus- and tram stops
% of citizens who thought that the destruction of phone booths, bus- and tram stops is a neighbourhood problem
2006-2011
Litter next to the container
% of citizens who thought that litter next to the container is a neighbourhood problem
2006-2011
Urinating in public
% of citizens who thought that urinating in public is a neighbourhood problem
2006-2011
Holes or cracks in pavement
% of citizens who thought that holes or cracks is a neighbourhood problem
2006-2011
Dog dirt on the streets
% of citizens who thought that dog dirt is a neighbourhood problem
2006-2011
Pollution on the streets
% of citizens who thought that pollution on the streets is a neighbourhood problem
2006-2011
Broken or destroyed street furniture
% of citizens who thought that broken or destroyed street furniture is a neighbourhood problem
2006-2011
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Fear of crime Feels unsafe in their own neighbourhood
% citizens who are feeling themselves unsafe in their own neighbourhood
2006-2011
Participation Employment or school Proportion of jobseekers of the total workforce. % of people aged between 16-22 years that is going to school of the total of youth without a basic qualification.
2008-2012
Social contacts of citizens % of residents that have at least weekly contact with friends and family. This contact may consist of a meeting, a phone call or e-mail. Social isolation.
2008-2012
Social and cultural activities
% of residents that sports at least one time per month; % of residents that goes out at least one time per month; % of residents that visits at least one time per month the theater, a concert or practices a creative hobby.
2008-2012
The social commitment of citizens
% of residents who helps someone who is long-term sick or in need of help; % of residents that who does volunteers in one or more organizations; % of residents that had an active contribution to the livability of the neighbourhood in the past twelve months.
2008-2012
Social ties Mutations % of removals in the neighbourhood; % of residents that lives shorter than 2 years in the Netherlands; % of residents that lives longer than ten years in the same house (in the case of new built houses counts 90 percent of the age of that house)
2008-2012
Experiencing bonding % of residents who feels themselves at home in the neighbourhood and thinks that the neighbors interact well with each other; % of residents that feels themselves responsible for the neighbourhood and commits themselves to the neighbourhood; % of residents that has confidence in the (district) council
2008-2012
Satisfaction with the neighbourhood
Satisfaction with the neighbourhood
% of residents that is satisfied with living in the neighbourhood
2008-2012
Table 4: List of all the variables
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Appendix C Table belonging to figures in chapter 4
2006 2007 2008 2009 2010 2011
Charlois 6955 6149 6516 6686 5836 5964
Delfshaven 6494 5908 6127 6131 5606 5818
Feijenoord 6038 6010 6077 5809 5264 5503
Hillegersberg-
Schiebroek
2603 2305 2531 2504 2298 2454
Hoek van Holland 584 504 566 760 524 582
Hoogvliet 2282 2234 2522 2243 1787 2360
IJsselmonde 4462 4506 4408 4424 3961 4412
Kralingen-Crooswijk 5235 4708 4868 4170 3607 3996
Noord 4835 4655 4633 4102 3679 3966
Overschie 1284 1113 1211 1232 1099 1165
Pernis 215 225 247 261 191 255
Prins Alexander 5503 5486 5877 6045 5041 6243
Stadscentrum 10935 11116 11481 9590 8212 8883
Rotterdam 57425 54919 57064 53957 47105 51601
Table 5: Total crime rates in the districts, Rotterdam, 2006-2011. Source: (processed data from) Police Rotterdam.
2006 2007 2008 2009 2010 2011
Theft and burglary crimes
Theft/burglary property 4607 3992 4235 3929 3934 4061
Theft/burglary box/garage 2768 1985 1861 1697 1540 1389
Pickpocketing 2546 2686 2667 2092 2069 2277
Mugging 1663 1450 1308 1143 1104 1196
Theft of motor vehicles 1832 1550 1446 1526 1397 1265
Theft out of/from motor
vehicles
9900 8648 10430 9500 8306 8203
Shoplifting 2827 2826 2798 3239 3317 3477
Theft on/off/from other
vehicles
821 1037 987 894 886 844
Theft of (light) mopeds and
bicycles
4296 4234 5219 4337 3624 4730
Raid 238 284 333 320 270 281
Other property crimes 8286 8063 8126 7675 7965 8738
Total 41790 38762 41418 38361 36422 38472
Table 6: Crime rates of theft and burglary crimes, 2006-2011, Rotterdam. Source (processed data from) Police Rotterdam.
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2006 2007 2008 2009 2010 2011
Destruction or material
damage against the public law
and order crimes
Destruction or material damage 7641 8430 8104 8381 4581 7101
Publicly violence 825 929 823 905 755 665
Undermining public order 590 476 374 399 536 371
Discrimination 25 29 20 27 37 7
Violence and sex crimes
Mistreatment 4321 4595 4579 4137 3726 3737
Murder, manslaughter 456 411 430 365 394 355
Sexual offense 689 618 552 515 544 522
Threat 2937 2821 2860 2872 2545 2641
Drugs and alcohol offenses
Drugs trafficking 1099 815 946 921 927 1122
Drugs/alcohol nuisance 27 34 17 7 20 33
Table 7: Crime rates of violence and sex crimes, and drugs and alcohol offenses, 2006-2011, Rotterdam. Source: (processed data from) Police Rotterdam.
Table 8: Index rates (2006=100), burglary and theft crimes, 2006-2011, Rotterdam. Source: (Processed data from) Police Rotterdam.
2006 2007 2008 2009 2010 2011
Theft and burglary
Charlois 100 83 90 93 86 83
Delfshaven 100 89 91 91 88 89
Feijenoord 100 95 98 93 89 89
Hillegersberg-
Schiebroek
100 79 101 94 102 103
Hoek van Holland 100 67 93 115 92 92
Hoogvliet 100 96 103 93 86 109
IJsselmonde 100 96 91 95 98 98
Kralingen-Crooswijk 100 86 93 77 73 77
Noord 100 91 94 80 78 81
Overschie 100 80 98 95 97 99
Pernis 100 111 132 131 113 143
Prins Alexander 100 95 111 112 105 123
Stadscentrum 100 101 107 85 71 80
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Table 9: Index rates (2006=100), Destruction or material damage against public law and order, 2006-2011, Rotterdam. Source: (Processed data from) Police Rotterdam.
Table 10: Index rates (2006=100), Violence and sex crimes, 2006-2011, Rotterdam. Source: (Processed data from) Police Rotterdam.
2006 2007 2008 2009 2010 2011
Violence and sex crimes
Charlois 100 102 102 89 83 82 Delfshaven 100 90 95 91 85 79 Feijenoord 100 98 96 90 84 90 Hillegersberg-Schiebroek 100 90 94 102 73 79 Hoek van Holland 100 124 71 164 137 141 Hoogvliet 100 106 146 116 110 89 IJsselmonde 100 113 113 107 82 102 Kralingen-Crooswijk 100 101 96 90 70 84 Noord 100 102 101 95 84 81 Overschie 100 107 92 84 86 63 Pernis 100 75 67 101 67 91 Prins Alexander 100 96 91 76 81 89 Stadscentrum 100 105 99 96 91 83
2006 2007 2008 2009 2010 2011
Destruction or material
damage against the public
law and order crimes
Charlois 100 97 102 116 68 93 Delfshaven 100 99 102 108 80 97 Feijenoord 100 120 113 109 75 89 Hillegersberg-Schiebroek 100 121 87 101 46 72 Hoek van Holland 100 121 110 156 62 103 Hoogvliet 100 101 117 108 43 97 IJsselmonde 100 106 112 101 58 95 Kralingen-Crooswijk 100 97 89 80 49 63 Noord 100 124 104 107 43 90 Overschie 100 87 81 106 43 76 Pernis 100 109 113 116 64 95 Prins Alexander 100 114 103 120 54 91 Stadscentrum 100 111 98 99 80 87
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Table 11: Index rates (2006=100), Drugs and alcohol offenses, 2006-2011, Rotterdam. Source: (Processed data from) Police Rotterdam.
2006 2007 2008 2009 2010 2011
Drugs and alcohol offenses
Charlois 100 76 83 91 93 111 Delfshaven 100 71 93 93 84 114 Feijenoord 100 83 113 120 131 157 Hillegersberg-Schiebroek 100 83 66 53 73 93 Hoek van Holland 100 80 200 80 140 60 Hoogvliet 100 56 60 52 34 50 IJsselmonde 100 100 154 180 129 174 Kralingen-Crooswijk 100 103 47 80 78 122 Noord 100 69 67 47 46 64 Overschie 100 316 183 66 200 233 Pernis 100 300 0 100 50 0 Prins Alexander 100 94 80 119 105 250 Stadscentrum 100 66 89 72 80 67
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Appendix D Tables belonging to chapter five
Table 12: Development of disorder in the districts, 2006-2011, Rotterdam: Source: (processed data from) Directie Veiligheid Rotterdam.
Table 13: Development of the indicators of disorder, 2006-2011, Rotterdam. Source: (processed data from) Directie Veiligheid Rotterdam.
2006 2007 2008 2009 2010
Charlois 19,8 20,1 23,9 24,5 23,1
Delfshaven 19,5 17,9 24,6 25,3 25,1
Feijenoord 17,5 17,9 25,6 25,7 23,3
Hillegersberg-Schiebroek 12,2 11,3 14,0 12,3 11,9
Hoek van Holland 7,4 6,6 10,1 12,6 11,3
Hoogvliet 11,3 11,6 18,0 16,6 15,4
IJsselmonde 13,8 15,0 20,6 17,2 18,0
Kralingen-Crooswijk 16,1 15,7 21,4 21,1 19,0
Noord 18,7 15,6 19,4 20,9 19,0
Overschie 12,4 13,7 16,3 16,1 15,2
Pernis 15,3 17,0 15,8 14,5 11,4
Prins Alexander 15,6 15,3 16,9 16,9 13,8
Stadscentrum 21,5 19,9 26,3 25,7 22,7
Rotterdam 15,5 15,2 19,5 19,2 17,6
Rotterdam social disorder 7,0 6,7 10,3 10,9 10,8
Rotterdam physical disorder 19,7 19,5 24,0 23,2 20,9
2006 2007 2008 2009 2010
Social indicators
Drug nuisance 7,1 6,2 9,2 9,6 9,1
Loitering 12,4 13,1 17,5 18,1 18,2
Drunken people 5,6 5,2 9,1 10,5 11,0
Harassment 2,9 2,4 5,3 5,6 6,0
Physical indicators
Graffiti 12,8 14,0 17,0 15,1 12,5
Destruction 19,7 18,4 20,3 16,4 11,6
Litter 19,9 19,9 32,3 32,3 30,8
Urinating 8,8 8,5 16,6 18,0 16,7
Holes 21,1 20,9 25,2 25,2 22,7
Dog dirt 37,7 37,3 36,4 34,6 32,9
Pollution 30,2 29,2 32,5 32,3 30,3
Broken street furniture 7,3 7,7 11,9 11,9 9,6